diff --git a/DCT.py b/DCT.py new file mode 100644 index 0000000..ed60848 --- /dev/null +++ b/DCT.py @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 11 17:10:43 2019 + +@author: Administrator +""" + + +import cv2 +import numpy as np + + +img = cv2.imread('a.png',0) +img1 = img.astype('float') + +def dct(m): + m = np.float32(m)/255.0 + return cv2.dct(m)*255 +#print(dct(img1).shape) +new_dct=dct(img1) +after_dct=[] +for i in range(len(new_dct)): + for j in range(len(new_dct[0])): + after_dct.append(int(new_dct[i][j])) +#print(new_dct) +#new_dct=new_dct.reshape(-1,1) +#print(len(after_dct)) +#print(after_dct[:600]) + + + diff --git a/F3.py b/F3.py new file mode 100644 index 0000000..b42304b --- /dev/null +++ b/F3.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jun 10 17:30:40 2019 + +@author: Administrator +""" + +from Jsteg import Jsteg + + +class F3(Jsteg): + def __init__(self): + Jsteg.__init__(self) + + def set_sequence_after_dct(self,sequence_after_dct): + self.sequence_after_dct=sequence_after_dct + sum_len=len(self.sequence_after_dct) + zero_len=len([i for i in self.sequence_after_dct if i==0]) + one_len=len([i for i in self.sequence_after_dct if i in (-1,1)]) + self.available_info_len=sum_len-zero_len-one_len # 不是特别可靠 + print ("Load>> 大约可嵌入",sum_len-zero_len-int(one_len/2),'bits') + print ("Load>> 最少可嵌入",self.available_info_len,'bits\n') + + def _write(self,index,data): + origin=self.sequence_after_dct[index] + if origin == 0: + return False + elif origin in (-1,1) and data==0: + self.sequence_after_dct[index]=0 + return False + + lower_bit=origin%2 + + if lower_bit==data: + pass + elif origin>0: + self.sequence_after_dct[index]=origin-1 + elif origin<0: + self.sequence_after_dct[index]=origin+1 + return True + + def _read(self,index): + if self.sequence_after_dct[index] != 0: + return self.sequence_after_dct[index]%2 + else: + return None + + +if __name__=="__main__": + f3=F3() + # 写 + sequence_after_dct=[-1,0,1]*100+[i for i in range(-7,500)] + f3.set_sequence_after_dct(sequence_after_dct) + info1=[0,1,0,1,1,0,1,0] + f3.write(info1) + # 读 + sequence_after_dct2=f3.get_sequence_after_dct() + f3.set_sequence_after_dct(sequence_after_dct2) + info2=f3.read() + print (info2) \ No newline at end of file diff --git a/F4.py b/F4.py new file mode 100644 index 0000000..a2d709e --- /dev/null +++ b/F4.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 11 16:10:51 2019 + +@author: Administrator +""" + +from Jsteg import Jsteg + + +class F4(Jsteg): + def __init__(self): + Jsteg.__init__(self) + + def set_sequence_after_dct(self,sequence_after_dct): + self.sequence_after_dct=sequence_after_dct + sum_len=len(self.sequence_after_dct) + zero_len=len([i for i in self.sequence_after_dct if i==0]) + one_len=len([i for i in self.sequence_after_dct if i in (-1,1)]) + self.available_info_len=sum_len-zero_len-one_len # 不是特别可靠 + print ("Load>> 大约可嵌入",sum_len-zero_len-int(one_len/2),'bits') + print ("Load>> 最少可嵌入",self.available_info_len,'bits\n') + + def _write(self,index,data): + origin=self.sequence_after_dct[index] + if origin == 0: + return False + elif origin == 1 and data==0: + self.sequence_after_dct[index]=0 + return False + + elif origin == -1 and data==1: + self.sequence_after_dct[index]=0 + return False + + lower_bit=origin%2 + + if origin >0: + if lower_bit!=data: + self.sequence_after_dct[index]=origin-1 + else: + if lower_bit==data: + self.sequence_after_dct[index]=origin+1 + return True + + + def _read(self,index): + if self.sequence_after_dct[index] >0: + return self.sequence_after_dct[index]%2 + elif self.sequence_after_dct[index]<0: + return (self.sequence_after_dct[index]+1)%2 + else: + return None + + +if __name__=="__main__": + f4=F4() + # 写 + sequence_after_dct=[-1,0,1]*100+[i for i in range(-7,500)] + f4.set_sequence_after_dct(sequence_after_dct) + info1=[0,1,0,1,1,0,1,0] + f4.write(info1) + # 读 + sequence_after_dct2=f4.get_sequence_after_dct() + f4.set_sequence_after_dct(sequence_after_dct2) + info2=f4.read() + print (info2) \ No newline at end of file diff --git a/Jsteg.py b/Jsteg.py new file mode 100644 index 0000000..f31eaa6 --- /dev/null +++ b/Jsteg.py @@ -0,0 +1,128 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Jun 10 15:26:43 2019 + +@author: Administrator +""" + +import math + +class Jsteg: + def __init__(self): + self.sequence_after_dct=None + + def set_sequence_after_dct(self,sequence_after_dct): + self.sequence_after_dct=sequence_after_dct + self.available_info_len=len([i for i in self.sequence_after_dct if i not in (-1,1,0)]) # 不是绝对可靠的 + print ("Load>> 可嵌入",self.available_info_len,'bits') + + def get_sequence_after_dct(self): + return self.sequence_after_dct + + def write(self,info): + """先嵌入信息的长度,然后嵌入信息""" + info=self._set_info_len(info) + info_len=len(info) + info_index=0 + im_index=0 + while True: + if info_index>=info_len: + break + data=info[info_index] + if self._write(im_index,data): + info_index+=1 + im_index+=1 + + + def read(self): + """先读出信息的长度,然后读出信息""" + _len,sequence_index=self._get_info_len() + info=[] + info_index=0 + + while True: + if info_index>=_len: + break + data=self._read(sequence_index) + if data!=None: + info.append(data) + info_index+=1 + sequence_index+=1 + + return info + + #===============================================================# + + def _set_info_len(self,info): + l=int(math.log(self.available_info_len,2))+1 + info_len=[0]*l + _len=len(info) + info_len[-len(bin(_len))+2:]=[int(i) for i in bin(_len)[2:]] + return info_len+info + + def _get_info_len(self): + l=int(math.log(self.available_info_len,2))+1 + len_list=[] + _l_index=0 + _seq_index=0 + while True: + if _l_index>=l: + break + _d=self._read(_seq_index) + if _d!=None: + len_list.append(str(_d)) + _l_index+=1 + _seq_index+=1 + _len=''.join(len_list) + _len=int(_len,2) + return _len,_seq_index + + # 注意经过DCT会有负值,此处最低有效位的嵌入方式与空域LSB略有不同 + def _write(self,index,data): + origin=self.sequence_after_dct[index] + if origin in (-1,1,0): + return False + + lower_bit=origin%2 + if lower_bit==data: + pass + elif origin>0: + if (lower_bit,data) == (0,1): + self.sequence_after_dct[index]=origin+1 + elif (lower_bit,data) == (1,0): + self.sequence_after_dct[index]=origin-1 + elif origin<0: + if (lower_bit,data) == (0,1): + self.sequence_after_dct[index]=origin-1 + elif (lower_bit,data) == (1,0): + self.sequence_after_dct[index]=origin+1 + + return True + + def _read(self,index): + if self.sequence_after_dct[index] not in (-1,1,0): + return self.sequence_after_dct[index]%2 + else: + return None +''' +import cv2 +import numpy as np + +def dct(m): + m = np.float32(m)/255.0 + return cv2.dct(m)*255 +''' + +if __name__=="__main__": + jsteg=Jsteg() + # 写 + sequence_after_dct=[-1,0,1]*100+[i for i in range(-7,500)] + #print(sequence_after_dct) + jsteg.set_sequence_after_dct(sequence_after_dct) + info1=[0,1,0,1,1,0,1,0] + jsteg.write(info1) + sequence_after_dct2=jsteg.get_sequence_after_dct() + # 读 + jsteg.set_sequence_after_dct(sequence_after_dct2) + info2=jsteg.read() + print (info2) \ No newline at end of file diff --git a/LSB/flag.txt b/LSB/flag.txt new file mode 100644 index 0000000..929e393 --- /dev/null +++ b/LSB/flag.txt @@ -0,0 +1 @@ +dajkhfahjkf \ No newline at end of file diff --git a/LSB/get_flag.txt b/LSB/get_flag.txt new file mode 100644 index 0000000..929e393 --- /dev/null +++ b/LSB/get_flag.txt @@ -0,0 +1 @@ +dajkhfahjkf \ No newline at end of file diff --git a/LSB/get_info.py b/LSB/get_info.py new file mode 100644 index 0000000..6ea65d3 --- /dev/null +++ b/LSB/get_info.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun May 19 12:43:26 2019 + +@author: Administrator +""" + +from PIL import Image + +def mod(x,y): + return x%y + +def toasc(strr): + return int(strr, 2) + + +#le为所要提取的信息的长度,str1为加密载体图片的路径,str2为提取文件的保存路径 +def func(le,str1,str2): + b="" + im = Image.open(str1) + lenth = le*8 + width,height = im.size[0],im.size[1] + count = 0 + for h in range(height): + for w in range(width): + #获得(w,h)点像素的值 + pixel = im.getpixel((w, h)) + #此处余3,依次从R、G、B三个颜色通道获得最低位的隐藏信息 + if count%3==0: + count+=1 + b=b+str((mod(int(pixel[0]),2))) + if count ==lenth: + break + if count%3==1: + count+=1 + b=b+str((mod(int(pixel[1]),2))) + if count ==lenth: + break + if count%3==2: + count+=1 + b=b+str((mod(int(pixel[2]),2))) + if count ==lenth: + break + if count == lenth: + break + + with open(str2,"w",encoding='utf-8') as f: + for i in range(0,len(b),8): + #以每8位为一组二进制,转换为十进制 + stra = toasc(b[i:i+8]) + #将转换后的十进制数视为ascii码,再转换为字符串写入到文件中 + #print((stra)) + f.write(chr(stra)) + print("完成信息提取!") + + + + +def main(): + #文件长度 + le = 11 + #含有隐藏信息的图片 + new = "new.png" + #信息提取出后所存放的文件 + get_info = "get_flag.txt" + func(le,new,get_info) + +if __name__=='__main__': + main() \ No newline at end of file diff --git a/LSB/hide_info.py b/LSB/hide_info.py new file mode 100644 index 0000000..94281bb --- /dev/null +++ b/LSB/hide_info.py @@ -0,0 +1,85 @@ +# -*- coding: utf-8 -*- +""" +Created on Sun May 19 11:20:05 2019 + +@author: Administrator +""" + +from PIL import Image + + +def plus(string): + #Python zfill() 方法返回指定长度的字符串,原字符串右对齐,前面填充0。 + return string.zfill(8) + +def get_key(strr): + #获取要隐藏的文件内容 + with open(strr,"rb") as f: + s = f.read() + string="" + for i in range(len(s)): + #逐个字节将要隐藏的文件内容转换为二进制,并拼接起来 + #1.先用ord()函数将s的内容逐个转换为ascii码 + #2.使用bin()函数将十进制的ascii码转换为二进制 + #3.由于bin()函数转换二进制后,二进制字符串的前面会有"0b"来表示这个字符串是二进制形式,所以用replace()替换为空 + #4.又由于ascii码转换二进制后是七位,而正常情况下每个字符由8位二进制组成,所以使用自定义函数plus将其填充为8位 + string=string+""+plus(bin(s[i]).replace('0b','')) + #print(string) + return string + +def mod(x,y): + return x%y + +#str1为载体图片路径,str2为隐写文件,str3为加密图片保存的路径 +def func(str1,str2,str3): + im = Image.open(str1) + #获取图片的宽和高 + width,height= im.size[0],im.size[1] + print("width:"+str(width)) + print("height:"+str(height)) + count = 0 + #获取需要隐藏的信息 + key = get_key(str2) + keylen = len(key) + for h in range(height): + for w in range(width): + pixel = im.getpixel((w,h)) + a=pixel[0] + b=pixel[1] + c=pixel[2] + if count == keylen: + break + #下面的操作是将信息隐藏进去 + #分别将每个像素点的RGB值余2,这样可以去掉最低位的值 + #再从需要隐藏的信息中取出一位,转换为整型 + #两值相加,就把信息隐藏起来了 + a= a-mod(a,2)+int(key[count]) + count+=1 + if count == keylen: + im.putpixel((w,h),(a,b,c)) + break + b =b-mod(b,2)+int(key[count]) + count+=1 + if count == keylen: + im.putpixel((w,h),(a,b,c)) + break + c= c-mod(c,2)+int(key[count]) + count+=1 + if count == keylen: + im.putpixel((w,h),(a,b,c)) + break + if count % 3 == 0: + im.putpixel((w,h),(a,b,c)) + im.save(str3) + +def main(): + #原图 + old = "old.png" + #处理后输出的图片路径 + new = "new.png" + #需要隐藏的信息 + enc = "flag.txt" + func(old,enc,new) + +if __name__=='__main__': + main() \ No newline at end of file diff --git a/LSB/new.png b/LSB/new.png new file mode 100644 index 0000000..2a79d1b Binary files /dev/null and b/LSB/new.png differ diff --git a/LSB/old.png b/LSB/old.png new file mode 100644 index 0000000..33b271b Binary files /dev/null and b/LSB/old.png differ diff --git a/PDF/PDF格式.md b/PDF/PDF格式.md new file mode 100644 index 0000000..f1fd739 --- /dev/null +++ b/PDF/PDF格式.md @@ -0,0 +1,282 @@ +# PDF格式学习 +## PDF简介 +* PDF是Portable Document Format 的缩写,可翻译为“便携文件格式”,由Adobe System Incorporated 公司在1992年发明。 + +* PDF文件是一种编程形式的文档格式,它所有显示的内容,都是通过相应的操作符进行绘制的。 +* PDF基本显示单元包括:文字,图片,矢量图,图片 +* PDF扩展单元包括:水印,电子署名,注释,表单,多媒体,3D +* PDF动作单元:书签,超链接(拥有动作的单元有很多个,包括电子署名,多媒体等等) +## PDF的优点 +* 一致性: +在所有可以打开PDF的机器上,展示的效果是完全一致,不会出现段落错乱、文字乱码这些排版问题。尤其是文档中,本身可以嵌入字体,避免了客户端没有对应字体,而导致文字显示不一致的问题。所以,在印刷行业,绝大多数用的都是PDF格式。 +* 不易修改: +用过PDF文件的人,都会知道,对已经保存之后的PDF文件,想要进行重新排版,基本上就不可能的,这就保证了从资料源发往外界的资料,不容易被篡改。 +* 安全性: +PDF文档可以进行加密,包括以下几种加密形式:文档打开密码,文档权限密码,文档证书密码,加密的方法包括:RC4,AES,通过加密这种形式,可以达到资料防扩散等目的。 +* 不失真: +PDF文件中,使用了矢量图,在文件浏览时,无论放大多少倍,都不会导致使用矢量图绘制的文字,图案的失真。 +* 支持多种压缩方式: +为了减少PDF文件的size,PDF格式支持各种压缩方式: asciihex,ascii85,lzw,runlength,ccitt,jbig2,jpeg(DCT),jpeg2000(jpx) +* 支持多种印刷标准: +支持PDF-A,PDF-X + +## PDF格式 +根据PDF官方指南,理解PDF格式可以从四个方面下手——**Objects**(对象)、**File structure**(物理文件结构)、**Document structure**(逻辑文件结构)、**Content streams**(内容流)。 + +### 对象 + +### 物理文件结构 +* 整体上分为文件头(Header)、对象集合(Body)、交叉引用表(Xref table)、文件尾(Trailer)四个部分,结构如图。修改过的PDF结构会有部分变化。 +* 未经修改 +![未经修改](https://img-blog.csdnimg.cn/20190526170017719.png#pic_center) +* 经修改 +![在这里插入图片描述](https://img-blog.csdnimg.cn/20190526170402806.png#pic_center) +#### 文件头 +* 文件头是PDF文件的第一行,格式如下: +``` +%PDF-1.7 +``` +* 这是个固定格式,表示这个PDF文件遵循的PDF规范版本,解析PDF的时候尽量支持高版本的规范,以保证支持大多数工具生成的PDF文件。1.7版本支持1.0-1.7之间的所有版本。 + +#### 对象集合 +* 这是一个PDF文件最重要的部分,文件中用到的所有对象,包括文本、图象、音乐、视频、字体、超连接、加密信息、文档结构信息等等,都在这里定义。格式如下: +``` +2 0 obj + ... +end obj +``` +* 一个对象的定义包含4个部分:前面的2是**对象序号**,其用来唯一标记一个对象;0是**生成号**,按照PDF规范,如果一个PDF文件被修改,那这个数字是累加的,它和对象序号一起标记是原始对象还是修改后的对象,但是实际开发中,很少有用这种方式修改PDF的,都是重新编排对象号;obj和endobj是对象的定义范围,可以抽象的理解为这就是一个左括号和右括号;省略号部分是PDF规定的任意合法对象。 +* 可以通过R关键字来引用任何一个对象,比如要引用上面的对象,可以使用2 0 R,需要主意的是,R关键字不仅可以引用一个已经定义的对象,还可以引用一个并**不存在的对象**,而且效果就和引用了一个空对象一样。 +* 对象主要有下面几种 + * **booleam** +用关键字true或false表示,可以是array对象的一个元素,或dictionary对象的一个条目。也可以用在PostScript计算函数里面,做为if或if esle的一个条件。 + * **numeric** +包括整形和实型,不支持非十进制数字,不支持指数形式的数字。 + 例: + 1)整数 123 4567 +111 -2 + 范围:正2的31次方-1到负的2的31次方 + 2)实数 12.3 0.8 +6.3 -4.01 -3. +.03 + 范围:±3.403 ×10的38次方 ±1.175 × 10的-38次方 + * 注意:如果整数超过表示范围将转化成实数,如果实数超过范围就会出错 + * **string** +由一系列0-255之间的字节组成,一个string总长度不能超过65535.string有以下两种方式: + * 十六进制字串 +由<>包含起来的一个16进制串,两位表示一个字符,不足两位用0补齐。 +例: \ 表示AA和BB两个字符 \ 表示AA和B0两个字符 + * 直接字串 +由()包含起来的一个字串,中间可以使用转义符"/"。 +例: + (abc) 表示abc + (a//) 表示a/ +转义符的定义如下: + +|转义字符| 含义| +|--------|--------| +|/n |换行| +/r |回车 +/t |水平制表符 +/b |退格 +/f |换页(Form feed (FF)) +/( |左括号 +/) |右括号 +// |反斜杠 +/ddd |八进制形式的字符 + + +* 对象类别(续) + + * **name** + 由一个前导/和后面一系列字符组成,最大长度为127。和string不同的是,name是**不可分割**的并且是**唯一**的,不可分割就是说一个name对象就是一个原子,比如/name,不能说n就是这个name的一个元素;唯一就是指两个相同的name一定代表同一个对象。从pdf1.2开始,除了ascii的0,别的都可以用一个#加两个十六进制的数字表示。 + 例: + /name 表示name + /name#20is 表示name is + /name#200 表示name 0 + * **array** +用[]包含的一组对象,可以是任何pdf对象(包括array)。虽然pdf只支持一维array,但可以通过array的嵌套实现任意维数的array(但是一个array的元素不能超过8191)。 + 例:[549 3.14 false (Ralph) /SomeName] + * **Dictionary** + 用"<<"和">>"包含的若干组条目,每组条目都由key和value组成,其中key必须是name对象,并且一个dictionary内的key是唯一的;value可以是任何pdf的合法对象(包括dictionary对象)。 + 例: + ``` + << /IntegerItem 12 + /StringItem (a string) + /Subdictionary + << /Item1 0.4 + /Item2 true + /LastItem (not!) + /VeryLastItem (OK) + >> + >> + ``` + * **stream** + 由一个字典和紧跟其后面的一组关键字stream和endstream以及这组关键字中间包含一系列字节组成。内容和string很相似,但有区别:stream可以分几次读取,分开使用不同的部分,string必须作为一个整体一次全部读取使用;string有长度限制,但stream却没有这个限制。一般较大的数据都用stream表示。需要注意的是,stream必须是间接对象,并且stream的字典必须是直接对象。从1.2规范以后,stream可以以外部文件形式存在,这种情况下,解析PDF的时候stream和endstream之间的内容就被忽略掉。 + 例: + ``` + dictionary + stream + …data… + endstream + ``` + stream字典中常用的字段如下: + + |字段名 |类型| 值| + |--------|--------|--------| + |Length| 整形|(必须)关键字stream和endstream之间的数据长度,endstream之前可能会有一个多余的EOL标记,这个不计算在数据的长度中。 + Filter |名字 或 数组 |(可选)Stream的编码算法名称(列表)。如果有多个,则数组中的编码算法列表顺序就是数据被编码的顺序。 + DecodeParms |字典 或 数组 |(可选)一个参数字典或由参数字典组成的一个数组,供Filter使用。如果仅有一个Filter并且这个Filter需要参数,除非这个Filter的所有参数都已经给了默认值,否则的话 DecodeParms必须设置给Filter。如果有多个Filter,并且任意一个Filter使用了非默认的参数, DecodeParms 必须是个数组,每个元素对应一个Filter的参数列表(如果某个Filter无需参数或所有参数都有了默认值,就用空对象代替)。 如果没有Filter需要参数,或者所有Filter的参数都有默认值,DecodeParms 就被忽略了。 + F |文件标识 |(可选)保存stream数据的文件。如果有这个字段, stream和endstream就被忽略,FFilter将会代替Filter, FDecodeParms将代替DecodeParms。Length字段还是表示stream和endstream之间数据的长度,但是通常此刻已经没有数据了,长度是0. + FFilter |名字 或 字典| (可选)和filter类似,针对外部文件。 + FDecodeParms |字典 或 数组| (可选)和DecodeParams类似,针对外部文件。 + + + +Stream的编码算法名称(列表)。如果有多个,则数组中的编码算法列表顺序就是数据被编码的顺序。且需要被编码。编码算法主要如下: + ![在这里插入图片描述](https://img-blog.csdnimg.cn/20190526185703968.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x5YzQ0ODEzNDE4,size_16,color_FFFFFF,t_70) +编码可视化主要显示为乱码,所以提供了隐藏信息的机会,如下图的steam内容为乱码。 + ![在这里插入图片描述](https://img-blog.csdnimg.cn/20190526185800381.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x5YzQ0ODEzNDE4,size_16,color_FFFFFF,t_70#pic_center) + * **NULL** + 用null表示,代表空。如果一个key的值为null,则这个key可以被忽略;如果引用一个不存在的object则等价于引用一个空对象。 + +#### 交叉引用表 +* 交叉引用表是PDf文件内部一种特殊的文件组织方式,可以很方便的根据对象号随机访问一个对象。其格式如下: +``` + xref + 0 1 + 0000000000 65535 f + 4 1 + 0000000009 00000 n + 8 3 + 0000000074 00000 n + 0000000120 00000 n + 0000000179 00000 n +``` + * 其中,xref是开始标志,表示以下为一个交叉引用表的内容;每个交叉引用表又可以分为若干个子段,每个子段的第一行是两个数字,第一个是对象起始号,后面是连续的对象个数,接着每行是这个子段的每个对象的具体信息——每行的前10个数字代表这个这个对象**相对文件头的偏移地址**,后面的5位数字是**生成号**(用于标记PDF的更新信息,和对象的生成号作用类似),最后一位f或n表示对象是否被使用(n表示使用,f表示被删除或没有用)。上面这个交叉引用表一共有3个子段,分别有1个,1个,3个对象,第一个子段的对象不可用,其余子段对象可用。 +#### 文件尾 +* 通过trailer可以快速的找到交叉引用表的位置,进而可以精确定位每一个对象;还可以通过它本身的字典还可以获取文件的一些全局信息(作者,关键字,标题等),加密信息,等等。具体形式如下: +``` + trailer + << + key1 value1 + key2 value2 + key3 value3 + … + >> + startxref + 553 + %%EOF + ``` + + + + +* trailer后面紧跟一个字典,包含若干键-值对。具体含义如下: + +|键 |值类型| 值说明| +|--------|--------|--------| +|Size| 整形数字| 所有间接对象的个数。一个PDF文件,如果被更新过,则会有多个对象集合、交叉引用表、trailer,最后一个trailer的这个字段记录了之前所有对象的个数。这个值必须是直接对象。| +|Prev |整形数字| 当文件有多个对象集合、交叉引用表和trailer时,才会有这个键,它表示前一个相对于文件头的偏移位置。这个值必须是直接对象。| +|Root |字典 |Catalog字典(文件的逻辑入口点)的对象号。必须是间接对象。| +|Encrypt |字典| 文档被保护时,会有这个字段,加密字典的对象号。| +|Info |字典 |存放文档信息的字典,必须是间接对象。| +|ID |数组 |文件的ID| + +* 上面代码中的startxref:后面的数字表示最后一个交叉引用表相对于文件起始位置的偏移量 +* %%EOF:文件结束符 +### 逻辑文件结构 +![在这里插入图片描述](https://img-blog.csdnimg.cn/20190526185950801.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2x5YzQ0ODEzNDE4,size_16,color_FFFFFF,t_70#pic_center) + +#### catalog根节点 +* catalog是整个PDF逻辑结构的根节点,这个可以通过trailer的Root字段定位,虽然简单,但是相当重要,因为这里是PDF文件物理结构和逻辑结构的连接点。Catalog字典包含的信息非常多,这里仅就最主要的几个字段做个说明。 + * Pages字段 +这是个必须字段,是PDF里面所有页面的描述集合。Pages字段本身是个字典,它里面又包含了一下几个主要字段: + + |字段 |类型 |值| + |--------|--------|--------| + Type |name| (必须)只能为Pages 。 + Parent |dictionary |(如果不是catalog里面指定的跟节点,则必须有,并且必须是间接对象) 当前节点的直接父节点。 + Kids |array |(必须)一个间接对象组成的数组,节点可能是page或page tree。 + Count |integer| (必须) page tree里面所包含叶子节点(page 对象)的个数。 + + 从以上字段可以看出,Pages最主要的功能就是组织所有的page对象。Page对象描述了一个PDF页面的属性、资源等信息。Page对象是一个字典,它主要包含一下几个重要的属性: + + |字段 |类型 |值| +|--------|--------|--------| +Type |name |(必须)必须是Page。 +Parent |dictionary| (必须;并且只能是间接对象)当前page节点的直接父节点page tree 。 +LastModified| date| (如果存在PieceInfo字段,就必须有,否则可选)记录当前页面被最后一次修改的日期和时间。 +Resources| dictionary| (必须; 可继承)记录了当前page用到的所有资源。如果当前页不用任何资源,则这是个空字典。忽略所有字段则表示继承父节点的资源。 +MediaBox |rectangle| (必须; 可继承)定义了要显示或打印页面的物理媒介的区域(default user space units) +CropBox |rectangle| (可选; 可继承)定义了一个可视区域,当前页被显示或打印的时候,它的内容会被这个区域裁剪。默认值就是 MediaBox。 +BleedBox|rectangle |(可选) 定义了一个区域,当输出设备是个生产环境( production environment)的时候,页面显示的内容会被裁剪。默认值是 CropBox. +Contents |stream or array| (可选) 描述页面内容的流。如果这个字段缺省,则页面上什么也不会显示。这个值可以是一个流,也可以是由几个流组成的一个数组。如果是数组,实际效果相当于所有的流是按顺序连在一起的一个流,这就允许PDF生成的时候可以随时插入图片或其他资源。流之间的分割只是词汇上的一个分割,并不是逻辑上或者组织形式的切割。 +Rotate |integer| (可选; 可继承) 顺时钟旋转的角度数,这个必须是90的整数倍,默认是0。 +Thumb| stream |(可选)定义当前页的缩略图。 +Annots| array| (可选) 和当前页面关联的注释。 +Metadata |stream| (可选) 当前页包含的元数据。 +一个简单例子: +``` +3 0 obj + << /Type /Page + /Parent 4 0 R + /MediaBox [ 0 0 612 792 ] + /Resources <> + /ProcSet [ /PDF ] + >> + /Contents 12 0 R + /Thumb 14 0 R + /Annots [ 23 0 R 24 0 R] + >> +endobj +``` +* Outlines字段 +Outline是PDF里面为了方便用户从PDF的一部分跳转到另外一部分而设计的,有时候也叫书签(Bookmark),它是一个树状结构,可以直观的把PDF文件结构展现给用户。用户可以通过鼠标点击来打开或者关闭某个outline项来实现交互,当打开一个outline时,用户可以看到它的所有子节点,关闭一个outline的时候,这个outline的所有子节点会自动隐藏。并且,在点击的时候,阅读器会自动跳转到outline对应的页面位置。Outlines包含以下几个字段: + + |字段 |类型 |值| +|--------|--------|--------| +Type |name |(可选)如果这个字段有值,则必须是Outlines。 +First |dictionary |(必须;必须是间接对象) 第一个顶层Outline item。 +Last |dictionary |(必须;必须是间接对象)最后一个顶层outline item。 +Count |integer |(必须)outline的所有层次的item的总数。 + +Outline是一个管理outline item的顶层对象,我们看到的,其实是outline item,这个里面才包含了文字、行为、目标区域等等。一个outline item主要有一下几个字段: + + |字段 |类型 |值| +|--------|--------|--------| +Title |text string| (必须)当前item要显示的标题。 +Parent |dictionary |(必须;必须是间接对象) outline层级中,当前item的父对象。如果item本身是顶级item,则父对象就是它本身。 +Prev| dictionary| (除了每层的第一个item外,其他item必须有这个字段;必须是间接对象)当前层级中,此item的前一个item。 +Next |dictionary| (除了每层的最后一个item外,其他item必须有这个字段;必须是间接对象)当前层级中,此item的后一个item。 +First |dictionary| (如果当前item有任何子节点,则这个字段是必须的;必须是间接对象) 当前item的第一个直接子节点。 +Last |dictionary| (如果当前item有任何子节点,则这个字段是必须的;必须是间接对象) 当前item的最后一个直接子节点。 +Dest |name,byte string, or array |(可选; 如果A字段存在,则这个不能被会略)当前的outline item被激活的时候,要显示的区域。 +A |dictionary| (可选; 如果Dest 字段存在,则这个不能被忽略)当前的outline item被激活的时候,要执行的动作。 + +* URI字段 +URI(uniform resource identifier),定义了文档级别的统一资源标识符和相关链接信息。目录和文档中的链接就是通过这个字段来处理的. +* Metadata字段 +文档的一些附带信息,用xml表示,符合adobe的xmp规范。这个可以方便程序不用解析整个文件就能获得文件的大致信息。 +* 其他 +Catalog字典中,常用的字段一般有以下一些: + + |字段 |类型 |值| +|--------|--------|--------| +Type |name| (必须)必须为Catalog。 +Version| name |(可选)PDF文件所遵循的版本号(如果比文件头指定的版本号高的话)。如果这个字段缺省或者文件头指定的版本比这里的高,那就以文件头为准。一个PDF生成程序可以通过更新这个字段的值来修改PDF文件版本号。 +Pages |dictionary| (必须并且必须为间接对象)当前文档的页面集合入口。 +PageLabels |number tree| (可选) number tree,定义了页面和页面label对应关系。 +Names| dictionary| (可选)文档的name字典。 +Dests |dictionary |(可选;必须是间接对象)name和相应目标对应关系字典。 +ViewerPreferences| dictionary| (可选)阅读参数配置字典,定义了文档被打开时候的行为。如果缺省,则使用阅读器自己的配置。 +PageLayout |name |(可选) 指定文档被打开的时候页面的布局方式。SinglePageDisplay 单页OneColumnDisplay 单列TwoColumnLeftDisplay 双列,奇数页在左TwoColumnRightDisplay 双列,奇数页在右TwoPageLeft 双页,奇数页在左TwoPageRight 双页,奇数页在右缺省值: SinglePage. +PageMode |name| (可选) 当文档被打开时,指定文档怎么显示UseNone 目录和缩略图都不显示UseOutlines 显示目录UseThumbs 显示缩略图FullScreen 全屏模式,没有菜单,任何其他窗口UseOC 显示Optional content group 面板UseAttachments显示附件面板缺省值: UseNone. +Outlines |dictionary| (可选;必须为间接对象)文档的目录字典 +Threads |array |(可选;必须为间接对象)文章线索字典组成的数组。 +OpenAction |array or dictionary| (可选) 指定一个区域或一个action,在文档打开的时候显示(区域)或者执行(action)。如果缺省,则会用默认缩放率显示第一页的顶部。 +AA |dictionary| (可选)一个附加的动作字典,在全局范围内定义了响应各种事件的action。 +URI| dictionary |(可选)一个URI字典包含了文档级别的URI action信息。 +AcroForm| dictionary |(可选)文档的交互式form (AcroForm)字典。 +Metadata |stream |(可选;必须是间接对象)文档包含的元数据流。 + + diff 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] TJ +11.5961 TL +T* +[ (ing) -237.982 (the) -239.012 (unit) -238.014 (with) -238.997 (a) -238.004 (mechanism) -238.004 (to) -239.009 (e) 15.0122 (xplicitly) -237.995 (model) -238.994 (dynamic\054) ] TJ +11.5969 TL +T* +[ (non\055linear) -268.014 (dependencies) -268.017 (between) -267.99 (channels) -268.019 (using) -268.004 (global) -268.009 (in\055) ] TJ +11.5961 TL +T* +[ (formation) -357.007 (can) -355.982 (ease) -357.007 (the) -356.992 (learning) -356.98 (process\054) -382.98 (and) -356.994 <7369676e690263616e746c79> ] TJ +11.5969 TL +T* +[ (enhance) -250 (the) -249.99 (representational) -250.012 (po) 24.986 (wer) -250.002 (of) -249.997 (the) -249.988 (netw) 10.0081 (ork\056) ] TJ +/R25 9.9626 Tf +16.5781 TL +T* +[ (Attention) -530 (and) -529.015 (gating) -529.995 (mechanisms\056) ] TJ +/R27 9.9626 Tf +164.824 0 Td +[ (Attention) -529.985 (can) -529.015 (be) ] TJ +-164.824 -11.5961 Td +[ (vie) 24.9836 (wed\054) -259.011 (broadly) 64.9892 (\054) -259.013 (as) -257.013 (a) -256.982 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-0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +/R164 7.87045 Tf +11.8059 25.9449 Td +[ (3) -0.70105 ] TJ +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99531 -1.65273 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.87109 0 Td +(4) Tj +ET +Q +2438.52 6078.9 m +2438.52 6482 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 265.502 646.62 Tm +[ (2) -0.70105 ] TJ +13.8527 TL +T* +[ (6) -0.70105 ] TJ +4.72148 TL +T* +[ (6) -0.70105 ] TJ +5.03711 TL +T* +[ (4) -0.70105 ] TJ +/R37 7.87045 Tf +5.24727 18.1375 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(1) Tj +/R41 7.87045 Tf +5.68477 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(1) Tj +/R39 7.87045 Tf +3.93594 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(128) Tj +-40.3359 -10.0773 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(3) Tj +/R41 7.87045 Tf +5.68477 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(3) Tj +/R39 7.87045 Tf +3.93594 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(128) Tj +-40.3359 -10.077 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(1) Tj +/R41 7.87045 Tf +5.68477 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(1) Tj +/R39 7.87045 Tf +3.93594 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(512) Tj +/R39 7.87045 Tf +-40.3359 -10.0781 Td +[ (f) -108.616 (c) -0.80031 (\073) -0.80031 ] TJ +/R37 7.87045 Tf +11.6043 0 Td +[ (\133) -0.79721 (32) ] TJ +/R39 7.87045 Tf +10.0566 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49844 0 Td +[ (512\135) -0.79721 ] TJ +/R164 7.87045 Tf +26.9824 35.7062 Td +[ (3) -0.70105 ] TJ +13.8516 TL +T* +[ (7) -0.70105 ] TJ +4.72266 TL +T* +[ (7) -0.70105 ] TJ +5.03711 TL +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99609 3.06914 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(4) Tj +ET +Q +3633.42 6078.9 m +3633.42 6482 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 371.018 646.62 Tm +[ (2) -0.70105 ] TJ +13.8527 TL +T* +[ (6) -0.70105 ] TJ +4.72148 TL +T* +[ (6) -0.70105 ] TJ +5.03711 TL +T* +[ (4) -0.70105 ] TJ +/R37 7.87045 Tf +5.24727 18.1375 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8504 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49766 0 Td +(1) Tj +/R41 7.87045 Tf +5.68477 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(1) Tj +/R39 7.87045 Tf +3.93594 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(256) Tj +-40.3359 -10.0773 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8504 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49766 0 Td +(3) Tj +/R41 7.87045 Tf +5.68477 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(3) Tj +/R39 7.87045 Tf +3.93594 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(256) Tj +/R39 7.87045 Tf +19.6766 0 Td +[ (C) -0.70105 ] TJ +/R37 7.87045 Tf +8.37383 0 Td +[ (\075) -278.819 (32) ] TJ +-68.3863 -10.077 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8504 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49766 0 Td +(1) Tj +/R41 7.87045 Tf +5.68477 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(1) Tj +/R39 7.87045 Tf +3.93594 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +(512) Tj +/R39 7.87045 Tf +-40.3359 -10.0781 Td +[ (f) -108.616 (c) -0.80031 (\073) -0.80031 ] TJ +/R37 7.87045 Tf +11.6043 0 Td +[ (\133) -0.79721 (32) ] TJ +/R39 7.87045 Tf +10.0566 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +[ (512\135) -0.80031 ] TJ +/R164 7.87045 Tf +59.4059 35.7062 Td +[ (3) -0.70105 ] TJ +13.8516 TL +T* +[ (7) -0.70105 ] TJ +4.72266 TL +T* +[ (7) -0.70105 ] TJ +5.03711 TL +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99609 3.06914 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(4) Tj +ET +Q +1100.44 6077.33 m +4871.52 6077.33 l +S +q +10 0 0 10 0 0 cm +BT +/R37 7.87045 Tf +1 0 0 1 120.12 585.453 Tm +(14) Tj +/R41 7.87045 Tf +9.61992 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(14) Tj +ET +Q +1557.15 5672.66 m +1557.15 6075.75 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 160.595 601.273 Tm +[ (2) -0.6995 ] TJ +14.1668 TL +T* +[ (4) -0.6995 ] TJ +/R37 7.87045 Tf +5.24727 8.37617 Td +[ (c) -0.40016 (on) 27.42 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8496 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(256) Tj +-40.3355 -10.077 Td 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+13.8523 TL +T* +[ (6) -0.70105 ] TJ +4.72266 TL +T* +[ (6) -0.70105 ] TJ +5.03711 TL +T* +[ (4) -0.70105 ] TJ +/R37 7.87045 Tf +5.24688 18.1383 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(256) Tj +-40.3367 -10.0781 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(3) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(3) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(256) Tj +-40.3367 -10.077 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1024) Tj +/R39 7.87045 Tf +-40.3367 -10.0773 Td +[ (f) -108.616 (c) -0.80031 (\073) -0.80031 ] TJ +/R37 7.87045 Tf +11.6047 0 Td +[ (\133) -0.79721 (64) ] TJ +/R39 7.87045 Tf +10.0574 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +[ (1024\135) -0.79721 ] TJ +/R164 7.87045 Tf +30.918 35.7066 Td +[ (3) -0.70105 ] TJ +13.8527 TL +T* +[ (7) -0.70105 ] TJ +4.72266 TL +T* +[ (7) -0.70105 ] TJ +5.03711 TL +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99648 3.06992 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(6) Tj +ET +Q +3633.42 5672.66 m +3633.42 6075.75 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 369.05 605.995 Tm +[ (2) -0.70105 ] TJ +13.8523 TL +T* +[ (6) -0.70105 ] TJ +4.72266 TL +T* +[ (6) -0.70105 ] TJ +5.03711 TL +T* +[ (4) -0.70105 ] TJ +/R37 7.87045 Tf +5.24727 18.1383 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +-40.3367 -10.0781 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(3) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(3) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +/R39 7.87045 Tf +23.6117 0 Td +[ (C) -0.70105 ] TJ +/R37 7.87045 Tf +8.37383 0 Td +[ (\075) -278.819 (32) ] TJ +-72.3223 -10.077 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1024) Tj +/R39 7.87045 Tf +-40.3367 -10.0773 Td +[ (f) -108.616 (c) -0.80031 (\073) -0.80031 ] TJ +/R37 7.87045 Tf +11.6043 0 Td +[ (\133) -0.79721 (64) ] TJ +/R39 7.87045 Tf +10.0578 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49727 0 Td +[ (1024\135) -0.80652 ] TJ +/R164 7.87045 Tf +63.3414 35.7066 Td +[ (3) -0.70105 ] TJ +13.8527 TL +T* +[ (7) -0.70105 ] TJ +4.72266 TL +T* +[ (7) -0.70105 ] TJ +5.03711 TL +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99609 3.06992 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(6) Tj +ET +Q +1100.44 5671.09 m +4871.52 5671.09 l +S +q +10 0 0 10 0 0 cm +BT +/R27 7.87045 Tf +1 0 0 1 125.805 544.828 Tm +(7) Tj +/R41 7.87045 Tf +3.93516 0 Td +[ <02> -0.80031 ] TJ +/R27 7.87045 Tf +6.12148 0 Td +(7) Tj +ET +Q +1557.15 5266.41 m +1557.15 5669.51 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 160.595 560.649 Tm +[ (2) -0.6995 ] TJ +14.1672 TL +T* +[ (4) -0.6995 ] TJ +/R37 7.87045 Tf +5.24727 8.37656 Td +[ (c) -0.40016 (on) 27.42 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8496 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +-40.3355 -10.0773 Td +[ (c) -0.40016 (on) 27.42 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8496 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(3) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(3) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +-40.3355 -10.0773 Td +[ (c) -0.40016 (on) 27.42 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8496 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(2048) Tj +/R164 7.87045 Tf +15.7418 25.9453 Td +[ (3) -0.70105 ] TJ +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.12148 -1.65273 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +6.99648 0 Td +(3) Tj +ET +Q +2438.52 5266.41 m +2438.52 5669.51 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 263.534 565.37 Tm +[ (2) -0.70105 ] TJ +13.8516 TL +T* +[ (6) -0.70105 ] TJ +4.72266 TL +T* +[ (6) -0.70105 ] TJ +5.03711 TL +T* +[ (4) -0.70105 ] TJ +/R37 7.87045 Tf +5.24688 18.1375 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +-40.3367 -10.077 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(3) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(3) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(512) Tj +-40.3367 -10.0773 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(2048) Tj +/R39 7.87045 Tf +-40.3367 -10.0773 Td +[ (f) -108.616 (c) -0.80031 (\073) -0.80031 ] TJ +/R37 7.87045 Tf +11.6047 0 Td +[ (\133) -0.79721 (128) ] TJ +/R39 7.87045 Tf +13.9922 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +[ (2048\135) -0.79721 ] TJ +/R164 7.87045 Tf +26.9828 35.7055 Td +[ (3) -0.70105 ] TJ +13.8516 TL +T* +[ (7) -0.70105 ] TJ +4.72266 TL +T* +[ (7) -0.70105 ] TJ +5.03711 TL +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99609 3.06914 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(3) Tj +ET +Q +3633.42 5266.41 m +3633.42 5669.51 l +S +q +10 0 0 10 0 0 cm +BT +/R164 7.87045 Tf +1 0 0 1 369.05 565.37 Tm +[ (2) -0.70105 ] TJ +13.8516 TL +T* +[ (6) -0.70105 ] TJ +4.72266 TL +T* +[ (6) -0.70105 ] TJ +5.03711 TL +T* +[ (4) -0.70105 ] TJ +/R37 7.87045 Tf +5.24727 18.1375 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1024) Tj +-40.3367 -10.077 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(3) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(3) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1024) Tj +/R39 7.87045 Tf +23.6117 0 Td +[ (C) -0.70105 ] TJ +/R37 7.87045 Tf +8.37383 0 Td +[ (\075) -278.819 (32) ] TJ +-72.3223 -10.0773 Td +[ (c) -0.40016 (on) 27.4185 (v) -0.80031 ] TJ +/R39 7.87045 Tf +15.8508 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +/R39 7.87045 Tf +3.93516 0 Td +[ (\073) -0.80031 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +(2048) Tj +/R39 7.87045 Tf +-40.3367 -10.0773 Td +[ (f) -108.616 (c) -0.80031 (\073) -0.80031 ] TJ +/R37 7.87045 Tf +11.6043 0 Td +[ (\133) -0.79721 (128) ] TJ +/R39 7.87045 Tf +13.9926 0 Td +[ (\073) -0.79721 ] TJ +/R37 7.87045 Tf +3.49805 0 Td +[ (2048\135) -0.80652 ] TJ +/R164 7.87045 Tf +59.4059 35.7055 Td +[ (3) -0.70105 ] TJ +13.8516 TL +T* +[ (7) -0.70105 ] TJ +4.72266 TL +T* +[ (7) -0.70105 ] TJ +5.03711 TL +T* +[ (5) -0.70105 ] TJ +/R41 7.87045 Tf +6.99609 3.06914 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.86992 0 Td +(3) Tj +ET +Q +1100.44 5264.84 m +4871.52 5264.84 l +S +q +10 0 0 10 0 0 cm +BT +/R37 7.87045 Tf +1 0 0 1 124.056 519.272 Tm +(1) Tj +/R41 7.87045 Tf +5.68398 0 Td +[ <02> -0.80031 ] TJ +/R37 7.87045 Tf +7.8707 0 Td +(1) Tj +ET +Q +1557.15 5162.49 m +1557.15 5263.26 l +S +q +10 0 0 10 0 0 cm +BT +/R27 7.87045 Tf +1 0 0 1 257.813 519.272 Tm +[ (global) -250.002 (a) 19.9954 (v) 14.9981 (erage) -249.986 (pool\054) ] TJ +/R37 7.87045 Tf +65.7344 0 Td +(1000) Tj +/R27 7.87045 Tf +15.7406 0 Td +(\055d) Tj +/R39 7.87045 Tf +8.52344 0 Td +[ (f) -108.616 (c) -0.80031 ] TJ +/R27 7.87045 Tf +8.10703 0 Td +[ (\054) -249.992 (softmax) ] TJ +ET +Q +1100.44 5160.92 m +4871.52 5160.92 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.9664 Tf +1 0 0 1 50.1121 497.073 Tm +[ (T) 79.9813 (able) -310.999 (1\072) -430.984 (\050) ] TJ +/R25 8.9664 Tf +36.3117 0 Td +(Left) Tj +/R27 8.9664 Tf +15.9332 0 Td +[ (\051) -310.984 (ResNet\05550\056) -490.996 (\050) ] TJ +/R25 8.9664 Tf +53.7578 0 Td +(Middle) Tj +/R27 8.9664 Tf +27.4012 0 Td +[ (\051) -310.984 (SE\055ResNet\05550\056) -490.996 (\050) ] TJ +/R25 8.9664 Tf +67.207 0 Td +(Right) Tj +/R27 8.9664 Tf +21.4199 0 Td +[ (\051) -310.984 (SE\055ResNeXt\05550) -310.014 (with) -311.005 (a) -309.987 (32) ] TJ +/R231 8.9664 Tf +101.288 0 Td +[ <02> -0.40026 ] TJ +/R27 8.9664 Tf +7.16719 0 Td +[ (4d) -311.014 (template\056) -490.982 (The) -310.992 (shapes) -310.997 (and) -309.99 (operations) -311.003 (with) ] TJ +-330.486 -10.6301 Td +[ <73706563690263> -212.015 (parameter) -210.986 (settings) -212.017 (of) -211.002 (a) -212.008 (residual) -212.019 (b) 19.9965 (uilding) -211.006 (block) -212 (are) -211.998 (listed) -211.017 (inside) -211.987 (the) -210.998 (brack) 10.0119 (ets) -211.995 (and) -212.008 (the) -210.998 (number) -212.014 (of) -212.006 (stack) 9.99826 (ed) -211.009 (blocks) -211.995 (in) -210.976 (a) -212.006 (stage) -212.011 (is) -211.02 (presented) ] TJ +10.6309 TL +T* +[ (outside\056) -309.987 (The) -250.002 (inner) -250.003 (brack) 10.0119 (ets) -249.984 (follo) 25.0093 (wing) -249.98 (by) ] TJ +/R35 8.9664 Tf +147.899 0 Td +(fc) Tj +/R27 8.9664 Tf +8.71523 0 Td +[ (indicates) -250.003 (the) -249.989 (output) -250.006 (dimension) -250.011 (of) -249.992 (the) -249.989 (tw) 10.02 (o) -249.978 (fully) -250.019 (connected) -250 (layers) -250.017 (in) -250.011 (an) -250 (SE) -250.011 (module\056) ] TJ +ET +Q +3.4228 w +801.434 4604.97 m +5172.23 4604.97 l +S +1788.7 4493.55 m +1788.7 4603.25 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 205.42 452.646 Tm +(original) Tj +ET +Q +2586.25 4493.55 m +2586.25 4603.25 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 286.793 452.646 Tm +(re\055implementation) Tj +ET +Q +3782.58 4493.55 m +3782.58 4603.25 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 436.641 452.646 Tm +(SENet) Tj +ET +Q +1790.41 4491.84 m +5172.23 4491.84 l +S +1788.7 4383.32 m +1788.7 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 184.182 441.623 Tm +[ (top\0551) -249.983 (err) 55.0011 (\056) ] TJ +ET +Q +2187.48 4383.32 m +2187.48 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 224.06 441.623 Tm +[ (top\0555) -249.984 (err) 55.0011 (\056) ] TJ +ET +Q +2586.25 4383.32 m +2586.25 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 263.938 441.623 Tm +[ (top\0551err) 54.9982 (\056) ] TJ +ET +Q +2985.02 4383.32 m +2985.02 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 303.815 441.623 Tm +[ (top\0555) -249.984 (err) 55.0011 (\056) ] TJ +ET +Q +3383.8 4383.32 m +3383.8 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 343.693 441.623 Tm +(GFLOPs) Tj +ET +Q +3782.58 4383.32 m +3782.58 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 387.368 441.623 Tm +[ (top\0551) -249.984 (err) 55.0011 (\056) ] TJ +ET +Q +4278.87 4383.32 m +4278.87 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 436.997 441.623 Tm +[ (top\0555) -249.981 (err) 55.0011 (\056) ] TJ +ET +Q +4775.16 4383.32 m +4775.16 4493.55 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 482.828 441.623 Tm +(GFLOPs) Tj +ET +Q +801.434 4381.61 m +5172.23 4381.61 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 85.2836 430.311 Tm +[ (ResNet\05550) -250.015 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 126.923 430.311 Tm +(10) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 135.491 430.311 Tm +(\135) Tj +ET +Q +1788.7 4270.2 m +1788.7 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 191.193 430.311 Tm +(24) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.79929 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(7) Tj +ET +Q +2187.48 4270.2 m +2187.48 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 233.212 430.311 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(8) Tj +ET +Q +2586.25 4270.2 m +2586.25 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 430.311 Tm +(24) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(80) Tj +ET +Q +2985.02 4270.2 m +2985.02 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 430.311 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(48) Tj +ET +Q +3383.8 4270.2 m +3383.8 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 350.704 430.311 Tm +(3) Tj +/R39 8.56784 Tf +4.28359 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(86) Tj +ET +Q +3782.58 4270.2 m +3782.58 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 430.311 Tm +(23) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(29) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (1) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (5) -0.50477 (1) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 4270.2 m +4278.87 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 430.311 Tm +(6) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(62) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (0) -0.50477 ] TJ +/R49 5.99747 Tf +6.09219 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (8) -0.50477 (6) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4775.16 4270.2 m +4775.16 4379.9 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 489.839 430.311 Tm +(3) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(87) Tj +/R27 8.56784 Tf +-411.219 -10.9711 Td +[ (ResNet\055101) -250.022 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 131.207 419.34 Tm +(10) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 139.775 419.34 Tm +(\135) Tj +ET +Q +1788.7 4160.49 m +1788.7 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 191.193 419.34 Tm +(23) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.79929 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(6) Tj +ET +Q +2187.48 4160.49 m +2187.48 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 233.212 419.34 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(1) Tj +ET +Q +2586.25 4160.49 m +2586.25 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 419.34 Tm +(23) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(17) Tj +ET +Q +2985.02 4160.49 m +2985.02 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 419.34 Tm +(6) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(52) Tj +ET +Q +3383.8 4160.49 m +3383.8 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 350.704 419.34 Tm +(7) Tj +/R39 8.56784 Tf +4.28359 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(58) Tj +ET +Q +3782.58 4160.49 m +3782.58 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 419.34 Tm +(22) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(38) Tj +/R162 5.99747 Tf +8.56836 -1.54648 Td +[ (\050) -0.39893 (0) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (7) -0.50477 (9) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 4160.49 m +4278.87 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 419.34 Tm +(6) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(07) Tj +/R162 5.99747 Tf +8.56836 -1.54648 Td +[ (\050) -0.39893 (0) -0.50477 ] TJ +/R49 5.99747 Tf +6.09219 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (4) -0.50477 (5) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4775.16 4160.49 m +4775.16 4270.19 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 489.839 419.34 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(60) Tj +/R27 8.56784 Tf +-411.219 -10.9703 Td +[ (ResNet\055152) -250.022 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 131.207 408.37 Tm +(10) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 139.775 408.37 Tm +(\135) Tj +ET +Q +1788.7 4050.79 m +1788.7 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 191.193 408.37 Tm +(23) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.79929 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(0) Tj +ET +Q +2187.48 4050.79 m +2187.48 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 233.212 408.37 Tm +(6) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(7) Tj +ET +Q +2586.25 4050.79 m +2586.25 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 408.37 Tm +(22) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(42) Tj +ET +Q +2985.02 4050.79 m +2985.02 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 408.37 Tm +(6) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(34) Tj +ET +Q +3383.8 4050.79 m +3383.8 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 348.561 408.37 Tm +(11) Tj +/R39 8.56784 Tf +8.56836 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.3793 0 Td +(30) Tj +ET +Q +3782.58 4050.79 m +3782.58 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 408.37 Tm +(21) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(57) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (0) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (8) -0.50477 (5) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 4050.79 m +4278.87 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 408.37 Tm +(5) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(73) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (0) -0.50477 ] TJ +/R49 5.99747 Tf +6.09219 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (6) -0.50477 (1) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4775.16 4050.79 m +4775.16 4160.49 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 487.697 408.37 Tm +(11) Tj +/R39 8.56784 Tf +8.56719 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.38047 0 Td +(32) Tj +ET +Q +801.434 4049.08 m +5172.23 4049.08 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 85.2836 397.057 Tm +[ (ResNeXt\05550) -250.01 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 133.109 397.057 Tm +(47) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 141.677 397.057 Tm +(\135) Tj +ET +Q +1788.7 3937.66 m +1788.7 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 191.193 397.057 Tm +(22) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.79929 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(2) Tj +ET +Q +2187.48 3937.66 m +2187.48 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 237.26 397.057 Tm +(\055) Tj +ET +Q +2586.25 3937.66 m +2586.25 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 397.057 Tm +(22) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(11) Tj +ET +Q +2985.02 3937.66 m +2985.02 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 397.057 Tm +(5) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(90) Tj +ET +Q +3383.8 3937.66 m +3383.8 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 350.704 397.057 Tm +(4) Tj +/R39 8.56784 Tf +4.28359 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(24) Tj +ET +Q +3782.58 3937.66 m +3782.58 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 397.057 Tm +(21) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(10) Tj +/R162 5.99747 Tf +8.56836 -1.54609 Td +[ (\050) -0.39893 (1) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (0) -0.50477 (1) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 3937.66 m +4278.87 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 397.057 Tm +(5) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(49) Tj +/R162 5.99747 Tf +8.56836 -1.54609 Td +[ (\050) -0.39893 (0) -0.50477 ] TJ +/R49 5.99747 Tf +6.09219 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (4) -0.50477 (1) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4775.16 3937.66 m +4775.16 4047.36 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 489.839 397.057 Tm +(4) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(25) Tj +/R27 8.56784 Tf +-411.219 -10.9703 Td +[ (ResNeXt\055101) -250.018 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 137.393 386.086 Tm +(47) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 145.961 386.086 Tm +(\135) Tj +ET +Q +1788.7 3827.95 m +1788.7 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 191.193 386.086 Tm +(21) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.79929 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(2) Tj +ET +Q +2187.48 3827.95 m +2187.48 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 233.212 386.086 Tm +(5) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(6) Tj +ET +Q +2586.25 3827.95 m +2586.25 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 386.086 Tm +(21) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(18) Tj +ET +Q +2985.02 3827.95 m +2985.02 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 386.086 Tm +(5) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(57) Tj +ET +Q +3383.8 3827.95 m +3383.8 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 350.704 386.086 Tm +(7) Tj +/R39 8.56784 Tf +4.28359 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(99) Tj +ET +Q +3782.58 3827.95 m +3782.58 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 386.086 Tm +(20) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(70) Tj +/R162 5.99747 Tf +8.56836 -1.54688 Td +[ (\050) -0.39893 (0) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (4) -0.50477 (8) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 3827.95 m +4278.87 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 386.086 Tm +(5) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(01) Tj +/R162 5.99747 Tf +8.56836 -1.54688 Td +[ (\050) -0.39893 (0) -0.50477 ] TJ +/R49 5.99747 Tf +6.09219 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (5) -0.50477 (6) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4775.16 3827.95 m +4775.16 3937.66 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 489.839 386.086 Tm +(8) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(00) Tj +ET +Q +801.434 3826.24 m +5172.23 3826.24 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 85.2836 374.774 Tm +[ (V) 15.0048 (GG\05516) -249.991 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 120.129 374.774 Tm +(39) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 128.697 374.774 Tm +(\135) Tj +ET +Q +1788.7 3714.83 m +1788.7 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 197.382 374.774 Tm +(\055) Tj +ET +Q +2187.48 3714.83 m +2187.48 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 237.26 374.774 Tm +(\055) Tj +ET +Q +2586.25 3714.83 m +2586.25 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 374.774 Tm +(27) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(02) Tj +ET +Q +2985.02 3714.83 m +2985.02 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 374.774 Tm +(8) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(81) Tj +ET +Q +3383.8 3714.83 m +3383.8 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 348.561 374.774 Tm +(15) Tj +/R39 8.56784 Tf +8.56836 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.3793 0 Td +(47) Tj +ET +Q +3782.58 3714.83 m +3782.58 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 374.774 Tm +(25) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(22) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (1) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (8) -0.50477 (0) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 3714.83 m +4278.87 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 374.774 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(70) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (1) -0.50477 ] TJ +/R49 5.99747 Tf +6.09219 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (1) -0.50477 (1) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4775.16 3714.83 m +4775.16 3824.53 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 487.697 374.774 Tm +(15) Tj +/R39 8.56784 Tf +8.56719 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.38047 0 Td +(48) Tj +/R27 8.56784 Tf +-413.361 -10.9711 Td +[ (BN\055Inception) -250.018 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 137.393 363.803 Tm +(16) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.56784 Tf +1 0 0 1 145.961 363.803 Tm +(\135) Tj +ET +Q +1788.7 3605.12 m +1788.7 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 191.193 363.803 Tm +(25) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.79929 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(2) Tj +ET +Q +2187.48 3605.12 m +2187.48 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 231.071 363.803 Tm +(7) Tj +/R39 8.56784 Tf +4.28359 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(82) Tj +ET +Q +2586.25 3605.12 m +2586.25 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 268.806 363.803 Tm +(25) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(38) Tj +ET +Q +2985.02 3605.12 m +2985.02 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 310.825 363.803 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(89) Tj +ET +Q +3383.8 3605.12 m +3383.8 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 350.704 363.803 Tm +(2) Tj +/R39 8.56784 Tf +4.28359 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(03) Tj +ET +Q +3782.58 3605.12 m +3782.58 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 384.282 363.803 Tm +(24) Tj +/R39 8.56784 Tf +8.56797 0 Td +[ (\072) -0.80071 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(23) Tj +/R162 5.99747 Tf +8.56836 -1.54727 Td +[ (\050) -0.39893 (1) -0.49663 ] TJ +/R49 5.99747 Tf +6.09297 0 Td +[ (\072) -0.30123 ] TJ +/R162 5.99747 Tf +2.03477 0 Td +[ (1) -0.50477 (5) -0.49663 (\051) -0.40707 ] TJ +ET +Q +4278.87 3605.12 m +4278.87 3714.82 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.56784 Tf +1 0 0 1 436.054 363.803 Tm +(7) Tj +/R39 8.56784 Tf +4.28437 0 Td +[ (\072) -0.79786 ] TJ +/R37 8.56784 Tf +2.37969 0 Td +(14) Tj +/R162 5.99747 Tf +8.56836 -1.54727 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0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 434.794 607.482 Tm +(224) Tj +/R41 8.46821 Tf +14.5836 0 Td +[ <02> -0.80148 ] TJ +/R37 8.46821 Tf +8.46875 0 Td +(224) Tj +ET +Q +4824.48 5943.73 m +4824.48 6150.73 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.27 607.482 Tm +(320) Tj +/R41 8.46821 Tf +14.5844 0 Td +[ <02> -0.80148 ] TJ +/R37 8.46821 Tf +8.46875 0 Td +(320) Tj +/R27 8.46821 Tf +14.8187 0 Td +(\057) Tj +/R37 8.46821 Tf +-35.6363 -9.85664 Td +(299) Tj +/R41 8.46821 Tf +14.5844 0 Td +[ <02> -0.80148 ] TJ +/R37 8.46821 Tf +8.46875 0 Td +(299) Tj +ET +Q +4230.64 5942.04 m +5418.3 5942.04 l +S +4228.95 5736.73 m +4228.95 5943.73 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 428.845 586.782 Tm +(top\0551) Tj +3.4125 -9.85664 Td +[ (err) 54.9966 (\056) ] TJ +ET +Q +4526.71 5736.73 m +4526.71 5943.73 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 458.621 586.782 Tm +(top\0555) Tj +3.41289 -9.85664 Td +[ (err) 54.9966 (\056) ] TJ +ET +Q +4824.48 5736.73 m +4824.48 5943.73 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 488.398 586.782 Tm +(top\0551) Tj +3.4125 -9.85664 Td +[ (err) 54.9966 (\056) ] TJ +ET +Q +5122.24 5736.73 m +5122.24 5943.73 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 518.174 586.782 Tm +(top\0555) Tj +3.41289 -9.85664 Td +[ (err) 54.9966 (\056) ] TJ +ET +Q +3142.58 5735.04 m +5418.3 5735.04 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 319.34 565.744 Tm +[ (ResNet\055152) -249.981 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 364.729 565.744 Tm +(10) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 373.198 565.744 Tm +(\135) Tj +ET +Q +4228.95 5624.91 m +4228.95 5733.33 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 430.256 565.744 Tm +(23) Tj +/R39 8.46821 Tf +8.46758 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(0) Tj +ET +Q +4526.71 5624.91 m +4526.71 5733.33 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 462.149 565.744 Tm +(6) Tj +/R39 8.46821 Tf +4.23438 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35195 0 Td +(7) Tj +ET +Q +4824.48 5624.91 m +4824.48 5733.33 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 565.744 Tm +(21) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(3) Tj +ET +Q +5122.24 5624.91 m +5122.24 5733.33 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 565.744 Tm +(5) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(5) Tj +/R27 8.46821 Tf +-208.948 -10.8426 Td +[ (ResNet\055200) -249.981 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 364.729 554.901 Tm +(11) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 373.198 554.901 Tm +(\135) Tj +ET +Q +4228.95 5516.48 m +4228.95 5624.91 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 430.256 554.901 Tm +(21) Tj +/R39 8.46821 Tf +8.46758 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(7) Tj +ET +Q +4526.71 5516.48 m +4526.71 5624.91 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 462.149 554.901 Tm +(5) Tj +/R39 8.46821 Tf +4.23438 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35195 0 Td +(8) Tj +ET +Q +4824.48 5516.48 m +4824.48 5624.91 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 554.901 Tm +(20) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(1) Tj +ET +Q +5122.24 5516.48 m +5122.24 5624.91 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 554.901 Tm +(4) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(8) Tj +/R27 8.46821 Tf +-208.948 -10.8426 Td +[ (Inception\055v3) -249.981 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 367.549 544.059 Tm +(44) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 376.018 544.059 Tm +(\135) Tj +ET +Q +4228.95 5408.05 m +4228.95 5516.48 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 436.373 544.059 Tm +(\055) Tj +ET +Q +4526.71 5408.05 m +4526.71 5516.48 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 466.149 544.059 Tm +(\055) Tj +ET +Q +4824.48 5408.05 m +4824.48 5516.48 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 544.059 Tm +(21) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(2) Tj +ET +Q +5122.24 5408.05 m +5122.24 5516.48 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 544.059 Tm +(5) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(6) Tj +/R27 8.46821 Tf +-208.948 -10.8426 Td +[ (Inception\055v4) -249.981 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 367.549 533.216 Tm +(42) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 376.018 533.216 Tm +(\135) Tj +ET +Q +4228.95 5299.63 m +4228.95 5408.05 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 436.373 533.216 Tm +(\055) Tj +ET +Q +4526.71 5299.63 m +4526.71 5408.05 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 466.149 533.216 Tm +(\055) Tj +ET +Q +4824.48 5299.63 m +4824.48 5408.05 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 533.216 Tm +(20) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(0) Tj +ET +Q +5122.24 5299.63 m +5122.24 5408.05 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 533.216 Tm +(5) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(0) Tj +/R27 8.46821 Tf +-208.948 -10.8438 Td +[ (Inception\055ResNet\055v2) -249.99 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 395.3 522.372 Tm +(42) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 403.768 522.372 Tm +(\135) Tj +ET +Q +4228.95 5191.2 m +4228.95 5299.63 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 436.373 522.373 Tm +(\055) Tj +ET +Q +4526.71 5191.2 m +4526.71 5299.63 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 466.149 522.373 Tm +(\055) Tj +ET +Q +4824.48 5191.2 m +4824.48 5299.63 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 522.373 Tm +(19) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(9) Tj +ET +Q +5122.24 5191.2 m +5122.24 5299.63 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 522.373 Tm +(4) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(9) Tj +/R27 8.46821 Tf +-208.948 -10.8426 Td +[ (ResNeXt\055101) -249.981 (\05064) ] TJ +/R41 8.46821 Tf +62.0879 0 Td +[ <02> -0.7986 ] TJ +/R27 8.46821 Tf +8.7043 0 Td +[ (4d\051) -249.987 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 406.357 511.53 Tm +(47) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 414.825 511.53 Tm +(\135) Tj +ET +Q +4228.95 5082.77 m +4228.95 5191.2 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 430.256 511.53 Tm +(20) Tj +/R39 8.46821 Tf +8.46758 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(4) Tj +ET +Q +4526.71 5082.77 m +4526.71 5191.2 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 462.149 511.53 Tm +(5) Tj +/R39 8.46821 Tf +4.23438 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35195 0 Td +(3) Tj +ET +Q +4824.48 5082.77 m +4824.48 5191.2 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 511.53 Tm +(19) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(1) Tj +ET +Q +5122.24 5082.77 m +5122.24 5191.2 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 511.53 Tm +(4) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(4) Tj +/R27 8.46821 Tf +-208.948 -10.843 Td +[ (DenseNet\055264) -249.984 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 373.189 500.687 Tm +(14) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 381.657 500.687 Tm +(\135) Tj +ET +Q +4228.95 4974.34 m +4228.95 5082.77 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 428.146 500.687 Tm +(22) Tj +/R39 8.46821 Tf +8.46758 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(15) Tj +ET +Q +4526.71 4974.34 m +4526.71 5082.77 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 460.032 500.687 Tm +(6) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35195 0 Td +(12) Tj +ET +Q +4824.48 4974.34 m +4824.48 5082.77 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 495.925 500.687 Tm +(\055) Tj +ET +Q +5122.24 4974.34 m +5122.24 5082.77 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 525.702 500.687 Tm +(\055) Tj +-206.362 -10.8426 Td +[ (Attention\05592) -249.996 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 367.558 489.845 Tm +(46) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 376.026 489.845 Tm +(\135) Tj +ET +Q +4228.95 4865.92 m +4228.95 4974.34 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 436.373 489.845 Tm +(\055) Tj +ET +Q +4526.71 4865.92 m +4526.71 4974.34 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 466.149 489.845 Tm +(\055) Tj +ET +Q +4824.48 4865.92 m +4824.48 4974.34 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 489.808 489.845 Tm +(19) Tj +/R39 8.46821 Tf +8.46836 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(5) Tj +ET +Q +5122.24 4865.92 m +5122.24 4974.34 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 521.702 489.845 Tm +(4) Tj +/R39 8.46821 Tf +4.23398 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(8) Tj +/R27 8.46821 Tf +-208.948 -10.8426 Td +[ (V) 110.982 (ery) -249.987 (Deep) -250.022 (PolyNet) -249.993 (\133) ] TJ +ET +Q +0 1 0 rg +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 390.126 479.002 Tm +(51) Tj +ET +Q +0 g +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 398.594 479.002 Tm +(\135) Tj +/R55 5.92773 Tf +4.93555 3.07305 Td +[ (y) -0.80313 ] TJ +ET +Q +4228.95 4757.49 m +4228.95 4865.92 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 436.373 479.002 Tm +(\055) Tj +ET +Q +4526.71 4757.49 m +4526.71 4865.92 l +S +q +10 0 0 10 0 0 cm +BT +/R27 8.46821 Tf +1 0 0 1 466.149 479.002 Tm +(\055) Tj +ET +Q +4824.48 4757.49 m +4824.48 4865.92 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 487.698 479.002 Tm +(18) Tj +/R39 8.46821 Tf +8.46758 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35273 0 Td +(71) Tj +ET +Q +5122.24 4757.49 m +5122.24 4865.92 l +S +q +10 0 0 10 0 0 cm +BT +/R37 8.46821 Tf +1 0 0 1 519.584 479.002 Tm +(4) Tj +/R39 8.46821 Tf +4.23477 0 Td +[ (\072) -0.80148 ] TJ +/R37 8.46821 Tf +2.35195 0 Td +(25) Tj +/R27 8.46821 Tf +-206.831 -10.8434 Td +[ (PyramidNet\055200) -249.984 (\133) ] TJ +ET +Q 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index 0000000..345cdd5 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/AugStegoDataLayer.py @@ -0,0 +1,183 @@ +# imports +import json +import time +import pickle +import scipy.misc +import skimage.io +import caffe + +import numpy as np +import os.path as osp + +from random import shuffle +#from PIL import Image + +import matplotlib.image as mpimg + + +class AugmentDataLayerSync(caffe.Layer): + + """ + This is a simple syncronous datalayer for inputting the augmented data layer on the fly + """ + + def setup(self, bottom, top): + + self.top_names = ['data', 'label'] + + # === Read input parameters === + + # params is a python dictionary with layer parameters. + params = eval(self.param_str) + + # Check the paramameters for validity. + check_params(params) + + # store input as class variables + self.batch_size = params['batch_size'] + + # Create a batch loader to load the images. + self.batch_loader = BatchLoader( params, None ) + + # === reshape tops === + # since we use a fixed input image size, we can shape the data layer + # once. Else, we'd have to do it in the reshape call. + top[0].reshape( self.batch_size, + 1, + params['im_shape'][0], + params['im_shape'][1] ) + + # Ground truth + top[1].reshape(self.batch_size) + + print_info( "AugmentStegoDataLayerSync", params ) + + def forward(self, bottom, top): + """ + Load data. + """ + for itt in range(self.batch_size): + # Use the batch loader to load the next image. + im, label = self.batch_loader.load_next_image() + + # Add directly to the caffe data layer + top[0].data[itt, 0, :, :] = im + top[1].data[itt] = label + + def reshape(self, bottom, top): + """ + There is no need to reshape the data, since the input is of fixed size + (rows and columns) + """ + pass + + def backward(self, top, propagate_down, bottom): + """ + These layers does not back propagate + """ + pass + + +class BatchLoader(object): + + """ + This class abstracts away the loading of images. + Images can either be loaded singly, or in a batch. The latter is used for + the asyncronous data layer to preload batches while other processing is + performed. + """ + + def __init__(self, params, result): + + self.result = result + self.batch_size = params['batch_size'] + self.root = params['root'] + self.im_shape = params['im_shape'] + self.trainMode = ( params['split'] == 'train' ) # determine the mode, if test, no augment + + # get list of image indexes. + list_file = params['split'] + '.txt' + TXT_FILE = osp.join( self.root, list_file ) + txt_lines = [ line.rstrip('\n') for line in open( TXT_FILE ) ] + + total_size = len( txt_lines ) + + assert total_size%2 == 0, "total_size must be even" + + self.images = [] + self.labels = np.zeros( ( total_size, ), dtype = np.int64 ) + self.indexlist = range( total_size ) + + for i in np.arange(total_size): + tmp = txt_lines[i].split() + self.images.append(tmp[0]) + self.labels[i] = int(tmp[1]) + + self._cur = 0 # current image + self._epoch = 0 # current epoch count, also used as the randomization seed + self._flp = 1 # Augment flip number, + self._rot = 0 # Augment rotation number + + print "BatchLoader initialized with {} images".format(len(self.indexlist)) + + def load_next_image( self ): + + """ + Load the next image in a batch + """ + # Did we finish an epoch + if self._cur == len(self.indexlist): + self._epoch += 1 + l = np.random.seed( self._epoch ) #randomize, aslo reproducible + l = np.random.permutation( len(self.indexlist)/2 ) + l2 = np.vstack( ( 2*l, 2*l + 1 )).T + self.indexlist = l2.reshape(len(self.indexlist),) + self._cur = 0 + + # Index list + index = self.indexlist[self._cur] + + #load an image + image_file_name = self.images[index] + + im = np.asarray( mpimg.imread( image_file_name )) + + #Determine the new fliplr and rot90 status, used it in the stego + if ( self.trainMode ): + if ( self._cur % 2 == 0 ): + self._flp = np.random.choice(2)*2 - 1 + self._rot = np.random.randint(4) + im = im[:,::self._flp] + im = np.rot90(im, self._rot) + + #load the ground truth + label = self.labels[index] + + self._cur += 1 + + return im, label + + +def check_params(params): + """ + A utility function to check the parameters for the data layers. + """ + assert 'split' in params.keys( + ), 'Params must include split (train, val, or test).' + + required = ['batch_size', 'root', 'im_shape'] + for r in required: + assert r in params.keys(), 'Params must include {}'.format(r) + + +def print_info(name, params): + """ + Ouput some info regarding the class + """ + print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format( + name, + params['split'], + params['batch_size'], + params['im_shape']) + + diff --git a/PhaseAwareNet_SRC/Caffe/HG_IMDB_JUNI04_Create.ipynb b/PhaseAwareNet_SRC/Caffe/HG_IMDB_JUNI04_Create.ipynb new file mode 100644 index 0000000..07aff76 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/HG_IMDB_JUNI04_Create.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[HG:] Train Set = 30000, Test = 10000\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "\n", + "import sys\n", + "\n", + "\n", + "N_TRN = 5000\n", + "N_TST = 5000\n", + "\n", + "DataBaseFolder = '/home/mchen/tmp/caffe/data/JStego/'\n", + "\n", + "BOSS2CoverPath = os.path.join( DataBaseFolder, '75') # BOSS2 cover subfolder\n", + "BOSS2StegoPath = os.path.join( DataBaseFolder, 'JUNI_0.4') # BOSS2 stego subfolder\n", + "\n", + "BOW2CoverPath = os.path.join( DataBaseFolder, 'BOWS2_75') # BOWS cover subfolder\n", + "BOW2StegoPath = os.path.join( DataBaseFolder, 'BOWS2_JUNI_0.4') # BOWS stego subfolder\n", + "\n", + "TxtListFolder = '/home/mchen/tmp/caffe/data/JStego/MiracleList/'\n", + "\n", + "np.random.seed(0) # reset the random seed\n", + "\n", + "RandomImages = np.random.permutation(10000) + 1\n", + "\n", + "print (\"[HG:] Train Set = %d, Test = %d\"%( 20000 + N_TRN * 2, N_TST * 2 ) )" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "with open('{}/train.txt'.format(TxtListFolder), 'w') as f:\n", + " for idx in range(N_TRN):\n", + " imageName = str( RandomImages[idx] ) + '.jpg'\n", + " f.write('{} 0\\n'.format(BOSS2CoverPath + '/' + imageName ) )\n", + " f.write('{} 1\\n'.format(BOSS2StegoPath + '/' + imageName ) )\n", + " \n", + " for idx in range(10000):\n", + " imageName = str( RandomImages[idx] ) + '.jpg'\n", + " f.write('{} 0\\n'.format(BOW2CoverPath + '/' + imageName ) )\n", + " f.write('{} 1\\n'.format(BOW2StegoPath + '/' + imageName ) ) \n", + " \n", + " f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "with open('{}/test.txt'.format(TxtListFolder), 'w') as f:\n", + " for idx in range(N_TST):\n", + " imageName = str( RandomImages[N_TRN + idx] ) + '.jpg'\n", + " f.write('{} 0\\n'.format(BOSS2CoverPath + '/' + imageName ) )\n", + " f.write('{} 1\\n'.format(BOSS2StegoPath + '/' + imageName ) )\n", + " f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/PhaseAwareNet_SRC/Caffe/PhaseAwareNet.ipynb b/PhaseAwareNet_SRC/Caffe/PhaseAwareNet.ipynb new file mode 100644 index 0000000..c39d6de --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/PhaseAwareNet.ipynb @@ -0,0 +1,15800 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Training and testing PhaseAwareNet V Net with Miracle DataBase" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "caffe_root = '../../' # this file should be run from {caffe_root}/examples/PhaseAware \n", + "\n", + "import sys\n", + "import lmdb\n", + "\n", + "sys.path.insert(0, caffe_root + 'python')\n", + "import caffe\n", + "import os\n", + "\n", + "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '3'\n", + "\n", + "#caffe.set_device(1)\n", + "caffe.set_mode_gpu()\n", + "\n", + "import numpy as np\n", + "from pylab import *\n", + "%matplotlib inline\n", + "import tempfile\n", + "\n", + "sys.path.append(\"../pycaffe/layers\") # the datalayers we will use are in this directory.\n", + "sys.path.append(\"../pycaffe\") # the tools file is in this folder\n", + "\n", + "import tools #this contains some tools that we need\n", + "\n", + "from __future__ import division\n", + "\n", + "from caffe import layers as L\n", + "from caffe import params as P\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "define the PhaseAwareNet, VNet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def VahidNet( train=True, num_conv0_kernels=4, batch_size = 40, learn_conv0=False, moving_average_fraction = 0.98):\n", + " \n", + " # set conv0 leraning parameters\n", + " if learn_conv0==False:\n", + " param_conv0 = [dict(lr_mult=0, decay_mult=0)] * 2\n", + " else:\n", + " param_conv0 = [dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)]\n", + " \n", + " param_bn = [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0),\n", + " dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)]\n", + " # define network model\n", + " n = caffe.NetSpec()\n", + " \n", + " \n", + " # Prepare the data source, using Mo's stego Aug data layer\n", + " subset = 'train' if train else 'test'\n", + " root = '/home/mchen/tmp/caffe/data/JStego/MiracleList/'\n", + " params = dict( batch_size = batch_size, im_shape = [512, 512], split = subset, root = root )\n", + "\n", + " n.data, n.label = L.Python( module = 'AugStegoDataLayer', layer = 'AugmentDataLayerSync', \n", + " ntop = 2, param_str = str( params ) )\n", + " \n", + " #n.data, n.label = L.Data(source=source, batch_size = batch_size, backend=1, ntop=2)\n", + " \n", + " n.conv0 = L.Convolution(n.data, kernel_size=5, stride=1, num_output=num_conv0_kernels, pad=2,\n", + " param = param_conv0, \n", + " weight_filler=dict(type='constant', value=0),\n", + " bias_filler=dict(type='constant', value=0))\n", + " \n", + " n.conv1 = L.Convolution(n.conv0, kernel_size=5, stride=1, num_output=8, pad=2,\n", + " param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0))\n", + " \n", + " n.abs1 = L.AbsVal(n.conv1)\n", + " \n", + " n.bn1 = L.BatchNorm(n.abs1, param=param_bn,\n", + " scale_filler=dict(type='constant', value=1),\n", + " bias_filler=dict(type='constant', value=0),\n", + " moving_average_fraction = moving_average_fraction,\n", + " eps=1e-4)\n", + " \n", + " n.tanh1 = L.TanH(n.bn1, in_place=True)\n", + " \n", + " n.conv2 = L.Convolution(n.bn1, kernel_size=5, stride=1, num_output=16, pad=2,\n", + " param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0))\n", + " \n", + " n.bn2 = L.BatchNorm(n.conv2, param=param_bn,\n", + " scale_filler=dict(type='constant', value=1),\n", + " bias_filler=dict(type='constant', value=0),\n", + " moving_average_fraction = moving_average_fraction,\n", + " eps=1e-4)\n", + " \n", + " n.tanh2 = L.TanH(n.bn2, in_place=True)\n", + " \n", + " n.sbp = L.SplitByPhase(n.tanh2)\n", + " \n", + " n.conv3 = L.Convolution(n.sbp, kernel_size=1, stride=1, num_output=128, pad=0,\n", + " param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0))\n", + " \n", + " n.bn3 = L.BatchNorm(n.conv3, param=param_bn,\n", + " scale_filler=dict(type='constant', value=1),\n", + " bias_filler=dict(type='constant', value=0),\n", + " moving_average_fraction = moving_average_fraction,\n", + " eps=1e-4)\n", + " \n", + " n.relu3 = L.ReLU(n.bn3, in_place=True)\n", + " \n", + " n.pool3 = L.Pooling(n.bn3, pool=P.Pooling.AVE, kernel_size=5, stride=2, pad=1)\n", + " \n", + " n.conv4 = L.Convolution(n.pool3, kernel_size=1, stride=1, num_output=256, pad=0,\n", + " param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0))\n", + " \n", + " n.bn4 = L.BatchNorm(n.conv4, param=param_bn,\n", + " scale_filler=dict(type='constant', value=1),\n", + " bias_filler=dict(type='constant', value=0),\n", + " moving_average_fraction = moving_average_fraction,\n", + " eps=1e-4)\n", + " \n", + " n.relu4 = L.ReLU(n.bn4, in_place=True)\n", + " \n", + " n.pool4 = L.Pooling(n.bn4, pool=P.Pooling.AVE, kernel_size=5, stride=2, pad=1)\n", + " \n", + " n.conv5 = L.Convolution(n.pool4, kernel_size=1, stride=1, num_output=512, pad=0,\n", + " param=[dict(lr_mult=1, decay_mult=0), dict(lr_mult=0, decay_mult=0)], \n", + " weight_filler=dict(type='gaussian', std=0.01),\n", + " bias_filler=dict(type='constant', value=0))\n", + " \n", + " n.bn5 = L.BatchNorm(n.conv5, param=param_bn,\n", + " scale_filler=dict(type='constant', value=1),\n", + " bias_filler=dict(type='constant', value=0),\n", + " moving_average_fraction = moving_average_fraction,\n", + " eps=1e-4)\n", + " \n", + " n.relu5 = L.ReLU(n.bn5, in_place=True)\n", + " \n", + " n.pool5 = L.Pooling(n.bn5, pool=P.Pooling.AVE, global_pooling=True)\n", + " \n", + " n.fc6 = L.InnerProduct(n.pool5, num_output=2, \n", + " param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],\n", + " weight_filler=dict(type='xavier'),\n", + " bias_filler=dict(type='constant', value=0.01))\n", + " \n", + " n.loss = L.SoftmaxWithLoss(n.fc6, n.label)\n", + " \n", + " n.acc = L.Accuracy(n.fc6, n.label)\n", + " \n", + " # write the net to a temporary file and return its filename\n", + " with tempfile.NamedTemporaryFile(delete=False) as f:\n", + " f.write(str(n.to_proto()))\n", + " return f.name\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Define the solver" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from caffe.proto import caffe_pb2\n", + "\n", + "def solver(train_net_path, test_net_path=None, base_lr=0.001, weight_decay = 0.01):\n", + " s = caffe_pb2.SolverParameter()\n", + "\n", + " # Specify locations of the train and (maybe) test networks.\n", + " s.train_net = train_net_path\n", + " if test_net_path is not None:\n", + " s.test_net.append(test_net_path)\n", + " s.test_interval = 1000000 # Test after every 1000 training iterations.\n", + " s.test_iter.append(1) # Test on 100 batches each time we test.\n", + "\n", + " # The number of iterations over which to average the gradient.\n", + " # Effectively boosts the training batch size by the given factor, without\n", + " # affecting memory utilization.\n", + " s.iter_size = 1\n", + " \n", + " s.max_iter = 1000000 # # of times to update the net (training iterations)\n", + " \n", + " # Solve using the stochastic gradient descent (SGD) algorithm.\n", + " # Other choices include 'Adam' and 'RMSProp'.\n", + " s.type = 'SGD'\n", + "\n", + " # Set the initial learning rate for SGD.\n", + " s.base_lr = base_lr\n", + "\n", + " # Set `lr_policy` to define how the learning rate changes during training.\n", + " # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n", + " # every `stepsize` iterations.\n", + " s.lr_policy = 'step'\n", + " s.gamma = 0.75 \n", + " s.stepsize = 15000 #Every 20 epoch\n", + "\n", + " # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n", + " # weighted average of the current gradient and previous gradients to make\n", + " # learning more stable. L2 weight decay regularizes learning, to help prevent\n", + " # the model from overfitting.\n", + " s.momentum = 0.9\n", + " s.weight_decay = weight_decay\n", + "\n", + " # Display the current training loss and accuracy every 1000 iterations.\n", + " s.display = 10\n", + "\n", + " # Snapshots are files used to store networks we've trained. Here, we'll\n", + " # snapshot every 10K iterations -- ten times during training.\n", + " s.snapshot = 6000\n", + " #s.snapshot_prefix = '/home/dde/Desktop/JpegTraningSnapshots/Vnet_BOSS_JUNI_04_QF75_KVM_lr1em3_wd5em4_120ep_NoSBP'\n", + " s.snapshot_prefix = '/home/mchen/tmp/caffe/examples/PhaseAwareNet/VahidNet'\n", + " \n", + " # Train on the GPU. Using the CPU to train large networks is very slow.\n", + " s.solver_mode = caffe_pb2.SolverParameter.GPU\n", + " \n", + " # Write the solver to a temporary file and return its filename.\n", + " with tempfile.NamedTemporaryFile(delete=False) as f:\n", + " f.write(str(s))\n", + " return f.name" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def run_solver(niter, solver, disp_interval=10):\n", + " \n", + " blobs = ('loss', 'acc')\n", + " loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n", + " for _ in blobs)\n", + " for it in range(niter):\n", + " for name, s in solvers:\n", + " s.step(1) # run a single SGD step in Caffe\n", + " loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy() for b in blobs)\n", + " if it % disp_interval == 0 or it + 1 == niter:\n", + " if len(s.test_nets):\n", + " test_output = s.test_nets[0].forward()\n", + " test_acc = test_output['acc']\n", + " test_loss = test_output['loss']\n", + " else:\n", + " test_acc = 0\n", + " \n", + " test_loss = 0\n", + " #print test_acc\n", + " n = 'stego'\n", + " loss_disp = '%s: train_loss=%.3f, train_acc=%2d%%, test_loss=%.3f, test_acc=%2d%%' % (n, loss[n][it], np.round(100*acc[n][it]), test_loss, np.round(100*test_acc))\n", + " #loss_disp = '; '.join('%s: loss=%.3f, train_acc=%2d%%, test_acc=%2d%%' %\n", + " # (n, loss[n][it], np.round(100*acc[n][it]), test_acc)\n", + " # for n, _ in solvers)\n", + " print '%3d) %s' % (it, loss_disp) \n", + " # Save the learned weights from both nets.\n", + " weight_dir = tempfile.mkdtemp()\n", + " weights = {}\n", + " for name, s in solvers:\n", + " filename = 'weights.%s.caffemodel' % name\n", + " weights[name] = os.path.join(weight_dir, filename)\n", + " s.net.save(weights[name])\n", + " return loss, acc, weights" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def get_vahidnet_solver(model=None, base_lr=1e-3, weight_decay=0.01,\n", + " num_conv0_kernels=4, learn_conv0=False, moving_average_fraction=0.95):\n", + " KV = np.array([[-1,2,-2,2,-1],[2,-6,8,-6,2],[-2,8,-12,8,-2],[2,-6,8,-6,2],[-1,2,-2,2,-1]], dtype=np.float32)/12\n", + " KM = np.array([[0,0,5.2,0,0],[0,23.4,36.4,23.4,0],[5.2,36.4,-261,36.4,5.2],[0,23.4,36.4,23.4,0],[0,0,5.2,0,0]], dtype=np.float32)/261\n", + " GH = np.array([[0.0562,-0.1354,0,0.1354,-0.0562],[0.0818,-0.1970,0,0.1970,-0.0818],[0.0926,-0.2233,0,0.2233,-0.0926],[0.0818,-0.1970,0,0.1970,-0.0818],[0.0562,-0.1354,0,0.1354,-0.0562]], dtype=np.float32)\n", + " GV = np.fliplr(GH).T.copy()\n", + " \n", + " assert(num_conv0_kernels>0 and num_conv0_kernels<5)\n", + " \n", + " trn_net = VahidNet(True, num_conv0_kernels=num_conv0_kernels,\n", + " learn_conv0=learn_conv0, moving_average_fraction = moving_average_fraction)\n", + " \n", + " val_net = VahidNet(False, num_conv0_kernels=num_conv0_kernels,\n", + " learn_conv0=learn_conv0, moving_average_fraction = moving_average_fraction)\n", + " \n", + " solver_path = solver(trn_net, val_net, base_lr=base_lr, weight_decay=weight_decay)\n", + " \n", + " shisnet_solver = caffe.get_solver(solver_path)\n", + " \n", + " if model != None:\n", + " shisnet_solver.net.copy_from(model)\n", + " else:\n", + " if learn_conv0:\n", + " for k in xrange(num_conv0_kernels):\n", + " norm_const = np.delete(shisnet_solver.net.params['conv0'][0].data[k,0,:,:].flatten(), 12).sum()\n", + " shisnet_solver.net.params['conv0'][0].data[k,0,:,:] /= norm_const\n", + " shisnet_solver.net.params['conv0'][0].data[k,0,2,2] = -1\n", + " else:\n", + " if num_conv0_kernels>0:\n", + " shisnet_solver.net.params['conv0'][0].data[0] = KV\n", + " if num_conv0_kernels>1:\n", + " shisnet_solver.net.params['conv0'][0].data[1] = KM\n", + " if num_conv0_kernels>2:\n", + " shisnet_solver.net.params['conv0'][0].data[2] = GH\n", + " if num_conv0_kernels>3:\n", + " shisnet_solver.net.params['conv0'][0].data[3] = GV\n", + " \n", + " return shisnet_solver" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Train the Vahid Net" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BatchLoader initialized with 30000 images\n", + "AugmentStegoDataLayerSync initialized for split: train, with bs: 40, im_shape: [512, 512].\n", + "BatchLoader initialized with 10000 images\n", + "AugmentStegoDataLayerSync initialized for split: test, with bs: 40, im_shape: [512, 512].\n", + "Running solvers for 150000 iterations...\n", + " 0) stego: train_loss=0.707, train_acc=50%, test_loss=0.830, test_acc=50%\n", + " 10) stego: train_loss=0.694, train_acc=50%, test_loss=0.709, test_acc=50%\n", + " 20) stego: train_loss=0.693, train_acc=52%, test_loss=0.706, test_acc=50%\n", + " 30) stego: train_loss=0.688, train_acc=50%, test_loss=0.695, test_acc=55%\n", + " 40) stego: train_loss=0.685, train_acc=52%, test_loss=0.685, test_acc=52%\n", + " 50) stego: train_loss=0.680, train_acc=65%, test_loss=0.688, test_acc=52%\n", + " 60) stego: train_loss=0.676, train_acc=55%, test_loss=0.693, test_acc=50%\n", + " 70) stego: train_loss=0.685, train_acc=52%, test_loss=0.788, test_acc=50%\n", + " 80) stego: train_loss=0.657, train_acc=55%, test_loss=0.759, test_acc=55%\n", + " 90) stego: train_loss=0.670, train_acc=57%, test_loss=0.846, test_acc=52%\n", + "100) stego: train_loss=0.633, train_acc=62%, test_loss=0.836, test_acc=52%\n", + "110) stego: train_loss=0.631, train_acc=68%, test_loss=0.624, test_acc=73%\n", + "120) stego: train_loss=0.615, train_acc=68%, test_loss=0.702, test_acc=62%\n", + "130) stego: train_loss=0.652, train_acc=52%, test_loss=0.658, test_acc=62%\n", + "140) stego: 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stego: train_loss=0.115, train_acc=95%, test_loss=0.140, test_acc=95%\n", + "149820) stego: train_loss=0.031, train_acc=100%, test_loss=0.106, test_acc=98%\n", + "149830) stego: train_loss=0.114, train_acc=98%, test_loss=0.237, test_acc=90%\n", + "149840) stego: train_loss=0.116, train_acc=98%, test_loss=0.312, test_acc=88%\n", + "149850) stego: train_loss=0.037, train_acc=100%, test_loss=0.432, test_acc=88%\n", + "149860) stego: train_loss=0.105, train_acc=98%, test_loss=0.164, test_acc=95%\n", + "149870) stego: train_loss=0.083, train_acc=98%, test_loss=0.215, test_acc=85%\n", + "149880) stego: train_loss=0.112, train_acc=98%, test_loss=0.439, test_acc=93%\n", + "149890) stego: train_loss=0.246, train_acc=93%, test_loss=0.082, test_acc=95%\n", + "149900) stego: train_loss=0.132, train_acc=93%, test_loss=0.298, test_acc=88%\n", + "149910) stego: train_loss=0.060, train_acc=98%, test_loss=0.156, test_acc=93%\n", + "149920) stego: train_loss=0.029, train_acc=100%, test_loss=0.078, test_acc=100%\n", + "149930) stego: train_loss=0.114, train_acc=95%, test_loss=0.071, test_acc=100%\n", + "149940) stego: train_loss=0.052, train_acc=98%, test_loss=0.111, test_acc=95%\n", + "149950) stego: train_loss=0.061, train_acc=100%, test_loss=0.218, test_acc=93%\n", + "149960) stego: train_loss=0.068, train_acc=98%, test_loss=0.080, test_acc=98%\n", + "149970) stego: train_loss=0.028, train_acc=100%, test_loss=0.081, test_acc=98%\n", + "149980) stego: train_loss=0.118, train_acc=95%, test_loss=0.383, test_acc=85%\n", + "149990) stego: train_loss=0.051, train_acc=98%, test_loss=0.165, test_acc=90%\n", + "149999) stego: train_loss=0.056, train_acc=100%, test_loss=0.142, test_acc=93%\n", + "Done.\n" + ] + } + ], + "source": [ + "niter = 150000 # 200 epochs\n", + "\n", + "\n", + "#model = 'JPGSteganalysis_KM_NVC_40000iter_wd5e3.caffemodel'\n", + "model = None\n", + "\n", + "base_lr = 0.001\n", + "weight_decay = 1e-2\n", + "\n", + "num_conv0_kernels = 4\n", + "learn_conv0 = False\n", + "moving_average_fraction = 0\n", + "\n", + "vahid_net_solver = get_vahidnet_solver( model=model, base_lr=base_lr, weight_decay=weight_decay,\n", + " num_conv0_kernels=num_conv0_kernels, learn_conv0=learn_conv0, \n", + " moving_average_fraction=moving_average_fraction)\n", + "\n", + "print 'Running solvers for %d iterations...' % niter\n", + "solvers = [('stego', vahid_net_solver)] \n", + "loss, acc, weights = run_solver(niter, solvers)\n", + "print 'Done.'\n", + "\n", + "train_loss = loss['stego']\n", + "train_acc = acc['stego']\n", + "stego_weights = weights['stego']\n", + "\n", + "# Delete solvers to save memory.\n", + "del vahid_net_solver, solvers" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# !cp {\"%s\" % stego_weights} JPGSteganalysis_Vnet_BOSS_UED_02_QF95_KVMGHV_lr1em3g085ep10k_wd1em2_300k_bs40_MAF095_S_OrigCS.caffemodel\n", + "!cp {\"%s\" % stego_weights} VahidNet_JUNI_04_Miracle_Aug.caffemodel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "plt.plot(train_loss)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "plt.plot(train_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# update_progress() : Displays or updates a console progress bar\n", + "## Accepts a float between 0 and 1. Any int will be converted to a float.\n", + "## A value under 0 represents a 'halt'.\n", + "## A value at 1 or bigger represents 100%\n", + "def update_progress(progress):\n", + " barLength = 10 # Modify this to change the length of the progress bar\n", + " status = \"\"\n", + " if isinstance(progress, int):\n", + " progress = float(progress)\n", + " if not isinstance(progress, float):\n", + " progress = 0\n", + " status = \"error: progress var must be float\\r\\n\"\n", + " if progress < 0:\n", + " progress = 0\n", + " status = \"Halt...\\r\\n\"\n", + " if progress >= 1:\n", + " progress = 1\n", + " status = \"Done...\\r\\n\"\n", + " block = int(round(barLength*progress))\n", + " text = \"\\rPercent: [{0}] {1}% {2}\".format( \"#\"*block + \"-\"*(barLength-block), progress*100, status)\n", + " sys.stdout.write(text)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def compute_refinement_moments (net, collection_iters = 2000):\n", + " bn1 = {}\n", + " bn1['mean'] = np.zeros((collection_iters, net.params['bn1'][2].data.shape[1]))\n", + " bn1['var'] = np.zeros((collection_iters, net.params['bn1'][2].data.shape[1]))\n", + " bn1['ma_mean'] = np.squeeze(net.params['bn1'][2].data).copy()\n", + " bn1['ma_var'] = np.squeeze(net.params['bn1'][3].data).copy()\n", + "\n", + " bn2 = {}\n", + " bn2['mean'] = np.zeros((collection_iters, net.params['bn2'][2].data.shape[1]))\n", + " bn2['var'] = np.zeros((collection_iters, net.params['bn2'][2].data.shape[1]))\n", + " bn2['ma_mean'] = np.squeeze(net.params['bn2'][2].data).copy()\n", + " bn2['ma_var'] = np.squeeze(net.params['bn2'][3].data).copy()\n", + "\n", + " bn3 = {}\n", + " bn3['mean'] = np.zeros((collection_iters, net.params['bn3'][2].data.shape[1]))\n", + " bn3['var'] = np.zeros((collection_iters, net.params['bn3'][2].data.shape[1]))\n", + " bn3['ma_mean'] = np.squeeze(net.params['bn3'][2].data).copy()\n", + " bn3['ma_var'] = np.squeeze(net.params['bn3'][3].data).copy()\n", + "\n", + " bn4 = {}\n", + " bn4['mean'] = np.zeros((collection_iters, net.params['bn4'][2].data.shape[1]))\n", + " bn4['var'] = np.zeros((collection_iters, net.params['bn4'][2].data.shape[1]))\n", + " bn4['ma_mean'] = np.squeeze(net.params['bn4'][2].data).copy()\n", + " bn4['ma_var'] = np.squeeze(net.params['bn4'][3].data).copy()\n", + "\n", + " bn5 = {}\n", + " bn5['mean'] = np.zeros((collection_iters, net.params['bn5'][2].data.shape[1]))\n", + " bn5['var'] = np.zeros((collection_iters, net.params['bn5'][2].data.shape[1]))\n", + " bn5['ma_mean'] = np.squeeze(net.params['bn5'][2].data).copy()\n", + " bn5['ma_var'] = np.squeeze(net.params['bn5'][3].data).copy()\n", + "\n", + " for it in xrange(collection_iters):\n", + " net.forward()\n", + "\n", + " bn1['mean'][it, :] = np.squeeze(net.params['bn1'][2].data).copy()\n", + " bn1['var'][it, :] = np.squeeze(net.params['bn1'][3].data).copy()\n", + "\n", + " bn2['mean'][it, :] = np.squeeze(net.params['bn2'][2].data).copy()\n", + " bn2['var'][it, :] = np.squeeze(net.params['bn2'][3].data).copy()\n", + "\n", + " bn3['mean'][it, :] = np.squeeze(net.params['bn3'][2].data).copy()\n", + " bn3['var'][it, :] = np.squeeze(net.params['bn3'][3].data).copy()\n", + "\n", + " bn4['mean'][it, :] = np.squeeze(net.params['bn4'][2].data).copy()\n", + " bn4['var'][it, :] = np.squeeze(net.params['bn4'][3].data).copy()\n", + "\n", + " bn5['mean'][it, :] = np.squeeze(net.params['bn5'][2].data).copy()\n", + " bn5['var'][it, :] = np.squeeze(net.params['bn5'][3].data).copy()\n", + "\n", + " update_progress((it+1)/collection_iters)\n", + "\n", + " bn1['rf_mean'] = np.median(bn1['mean'], axis=0)\n", + " bn1['rf_var'] = np.median(bn1['var'], axis=0)\n", + "\n", + " bn2['rf_mean'] = np.median(bn2['mean'], axis=0)\n", + " bn2['rf_var'] = np.median(bn2['var'], axis=0)\n", + "\n", + " bn3['rf_mean'] = np.median(bn3['mean'], axis=0)\n", + " bn3['rf_var'] = np.median(bn3['var'], axis=0)\n", + "\n", + " bn4['rf_mean'] = np.median(bn4['mean'], axis=0)\n", + " bn4['rf_var'] = np.median(bn4['var'], axis=0)\n", + "\n", + " bn5['rf_mean'] = np.median(bn5['mean'], axis=0)\n", + " bn5['rf_var'] = np.median(bn5['var'], axis=0)\n", + " return bn1, bn2, bn3, bn4, bn5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def embed_refinement_moments(net, bn1, bn2, bn3, bn4, bn5):\n", + " net.params['bn1'][2].data[0,:,0,0] = bn1['rf_mean'].copy()\n", + " net.params['bn1'][3].data[0,:,0,0] = bn1['rf_var'].copy()\n", + "\n", + " net.params['bn2'][2].data[0,:,0,0] = bn2['rf_mean'].copy()\n", + " net.params['bn2'][3].data[0,:,0,0] = bn2['rf_var'].copy()\n", + "\n", + " net.params['bn3'][2].data[0,:,0,0] = bn3['rf_mean'].copy()\n", + " net.params['bn3'][3].data[0,:,0,0] = bn3['rf_var'].copy()\n", + "\n", + " net.params['bn4'][2].data[0,:,0,0] = bn4['rf_mean'].copy()\n", + " net.params['bn4'][3].data[0,:,0,0] = bn4['rf_var'].copy()\n", + "\n", + " net.params['bn5'][2].data[0,:,0,0] = bn5['rf_mean'].copy()\n", + " net.params['bn5'][3].data[0,:,0,0] = bn5['rf_var'].copy() " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def eval_stego_net( weights, test_iters=None, refine_bn=True):\n", + " \n", + " if refine_bn:\n", + " net = VahidNet( True, batch_size = 64, moving_average_fraction = 0)\n", + " net = caffe.Net(net, weights, caffe.TRAIN)\n", + " bn1, bn2, bn3, bn4, bn5 = compute_refinement_moments(net, collection_iters = 3000)\n", + " del net\n", + " \n", + " net = VahidNet(False)\n", + " \n", + " test_net = caffe.Net(net, weights, caffe.TEST)\n", + " \n", + " if refine_bn:\n", + " embed_refinement_moments(test_net, bn1, bn2, bn3, bn4, bn5)\n", + " \n", + " batch_size = test_net.blobs['data'].shape[0]\n", + " num_entries = 8000\n", + " if test_iters==None:\n", + " test_iters = int(np.floor(num_entries/batch_size))\n", + " \n", + " print 'Number of test samples: ', num_entries\n", + " print 'Testing for %d iterations using batch size %d...' % (test_iters, batch_size)\n", + " \n", + " accuracy = 0\n", + " for it in xrange(test_iters):\n", + " accuracy += test_net.forward()['acc']\n", + " update_progress((it+1)/test_iters)\n", + " accuracy /= test_iters\n", + " del test_net\n", + " \n", + " return accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BatchLoader initialized with 30000 images\n", + "AugmentStegoDataLayerSync initialized for split: train, with bs: 64, im_shape: [512, 512].\n", + "Percent: [##########] 100% Done...7% \n", + "BatchLoader initialized with 10000 images\n", + "AugmentStegoDataLayerSync initialized for split: test, with bs: 40, im_shape: [512, 512].\n", + "Number of test samples: 8000\n", + "Testing for 200 iterations using batch size 40...\n", + "Percent: [##########] 96.0% " + ] + } + ], + "source": [ + "weights = 'VahidNet_JUNI_04_Miracle_Aug.caffemodel'\n", + "\n", + "accuracy = eval_stego_net( weights, refine_bn=True)\n", + "\n", + "print 'Accuracy: ', accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# Keep training\n", + "\n", + "niter = 12000 # number of iterations to train 40000\n", + "\n", + "model = 'VahidNet_JUNI_04_AUG.caffemodel'\n", + "\n", + "base_lr = 0.001\n", + "\n", + "# Bring down the base_lr to be last one\n", + "for i in range(72000//6000 + 1):\n", + " base_lr = base_lr * 0.75\n", + "\n", + "num_conv0_kernels = 4\n", + "learn_conv0 = False\n", + "moving_average_fraction = 0\n", + "\n", + "vahid_net_solver, num_trn_entries, num_val_entries = get_vahidnet_solver(\n", + " trn_source=trn_source, val_source=val_source, model=model, \n", + " base_lr=base_lr, weight_decay=weight_decay,\n", + " num_conv0_kernels=num_conv0_kernels, learn_conv0=learn_conv0, \n", + " moving_average_fraction=moving_average_fraction)\n", + "\n", + "print 'Running solvers for %d iterations...' % niter\n", + "solvers = [('stego', vahid_net_solver)] \n", + "loss, acc, weights = run_solver(niter, solvers)\n", + "print 'Done.'\n", + "\n", + "train_loss = loss['stego']\n", + "train_acc = acc['stego']\n", + "stego_weights = weights['stego']\n", + "\n", + "# Delete solvers to save memory.\n", + "del vahid_net_solver, solvers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "!cp {\"%s\" % stego_weights} VahidNet_JUNI_04_AUG_more.caffemodel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "TRN_Source = '/home/mchen/tmp/caffe/examples/StegoLibrary/JUNI_04_AUG/JUNI_0.4_trn_lmdb'\n", + "TST_Source = '/home/mchen/tmp/caffe/examples/StegoLibrary/JUNI_04_AUG/JUNI_0.4_val_lmdb'\n", + "\n", + "weights = 'VahidNet_JUNI_04_AUG_more.caffemodel'\n", + "\n", + "accuracy = eval_stego_net( TRN_Source, TST_Source, weights, refine_bn=True)\n", + "\n", + "print 'Accuracy: ', accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "description": "Define, train, and test the classic LeNet with the Python interface.", + "example_name": "Learning LeNet", + "include_in_docs": true, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "priority": 2 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/PhaseAwareNet_SRC/Caffe/phase_aware_net_solver.prototxt b/PhaseAwareNet_SRC/Caffe/phase_aware_net_solver.prototxt new file mode 100644 index 0000000..81f2777 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/phase_aware_net_solver.prototxt @@ -0,0 +1,17 @@ +train_net: "/tmp/tmpNMRrso" +test_net: "/tmp/tmpwTrC5X" +test_iter: 1 +test_interval: 1000000 +base_lr: 0.0010000000475 +display: 10 +max_iter: 1000000 +lr_policy: "step" +gamma: 0.75 +momentum: 0.899999976158 +weight_decay: 0.00999999977648 +stepsize: 6000 +snapshot: 6000 +snapshot_prefix: "/home/mchen/tmp/caffe/examples/PhaseAwareNet" +solver_mode: GPU +iter_size: 1 +type: "SGD" diff --git a/PhaseAwareNet_SRC/Caffe/phase_aware_net_train.prototxt b/PhaseAwareNet_SRC/Caffe/phase_aware_net_train.prototxt new file mode 100644 index 0000000..7f3a285 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/phase_aware_net_train.prototxt @@ -0,0 +1,463 @@ +layer { + name: "data" + type: "Python" + top: "data" + top: "label" + python_param { + module: "AugStegoDataLayer" + layer: "AugmentDataLayerSync" + param_str: "{\'im_shape\': [512, 512], \'root\': \'/home/mchen/tmp/caffe/data/JStego/JUNI_0.4/\', \'split\': \'train\', \'batch_size\': 40}" + } +} +layer { + name: "conv0" + type: "Convolution" + bottom: "data" + top: "conv0" + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 4 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "constant" + value: 0.0 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv1" + type: "Convolution" + bottom: "conv0" + top: "conv1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 8 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.00999999977648 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "abs1" + type: "AbsVal" + bottom: "conv1" + top: "abs1" +} +layer { + name: "bn1" + type: "BatchNorm" + bottom: "abs1" + top: "bn1" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + batch_norm_param { + moving_average_fraction: 0.980000019073 + eps: 9.99999974738e-05 + scale_filler { + type: "constant" + value: 1.0 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "tanh1" + type: "TanH" + bottom: "bn1" + top: "bn1" +} +layer { + name: "conv2" + type: "Convolution" + bottom: "bn1" + top: "conv2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 16 + pad: 2 + kernel_size: 5 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.00999999977648 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "bn2" + type: "BatchNorm" + bottom: "conv2" + top: "bn2" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + batch_norm_param { + moving_average_fraction: 0.980000019073 + eps: 9.99999974738e-05 + scale_filler { + type: "constant" + value: 1.0 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "tanh2" + type: "TanH" + bottom: "bn2" + top: "bn2" +} +layer { + name: "sbp" + type: "SplitByPhase" + bottom: "bn2" + top: "sbp" +} +layer { + name: "conv3" + type: "Convolution" + bottom: "sbp" + top: "conv3" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.00999999977648 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "bn3" + type: "BatchNorm" + bottom: "conv3" + top: "bn3" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + batch_norm_param { + moving_average_fraction: 0.980000019073 + eps: 9.99999974738e-05 + scale_filler { + type: "constant" + value: 1.0 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "relu3" + type: "ReLU" + bottom: "bn3" + top: "bn3" +} +layer { + name: "pool3" + type: "Pooling" + bottom: "bn3" + top: "pool3" + pooling_param { + pool: AVE + kernel_size: 5 + stride: 2 + pad: 1 + } +} +layer { + name: "conv4" + type: "Convolution" + bottom: "pool3" + top: "conv4" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 256 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.00999999977648 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "bn4" + type: "BatchNorm" + bottom: "conv4" + top: "bn4" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + batch_norm_param { + moving_average_fraction: 0.980000019073 + eps: 9.99999974738e-05 + scale_filler { + type: "constant" + value: 1.0 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "relu4" + type: "ReLU" + bottom: "bn4" + top: "bn4" +} +layer { + name: "pool4" + type: "Pooling" + bottom: "bn4" + top: "pool4" + pooling_param { + pool: AVE + kernel_size: 5 + stride: 2 + pad: 1 + } +} +layer { + name: "conv5" + type: "Convolution" + bottom: "pool4" + top: "conv5" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + convolution_param { + num_output: 512 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "gaussian" + std: 0.00999999977648 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "bn5" + type: "BatchNorm" + bottom: "conv5" + top: "bn5" + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 1.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + param { + lr_mult: 0.0 + decay_mult: 0.0 + } + batch_norm_param { + moving_average_fraction: 0.980000019073 + eps: 9.99999974738e-05 + scale_filler { + type: "constant" + value: 1.0 + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "relu5" + type: "ReLU" + bottom: "bn5" + top: "bn5" +} +layer { + name: "pool5" + type: "Pooling" + bottom: "bn5" + top: "pool5" + pooling_param { + pool: AVE + global_pooling: true + } +} +layer { + name: "fc6" + type: "InnerProduct" + bottom: "pool5" + top: "fc6" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 0.0 + } + inner_product_param { + num_output: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0.00999999977648 + } + } +} +layer { + name: "loss" + type: "SoftmaxWithLoss" + bottom: "fc6" + bottom: "label" + top: "loss" +} +layer { + name: "acc" + type: "Accuracy" + bottom: "fc6" + bottom: "label" + top: "acc" +} diff --git a/PhaseAwareNet_SRC/Caffe/readme.docx b/PhaseAwareNet_SRC/Caffe/readme.docx new file mode 100644 index 0000000..ae3e598 Binary files /dev/null and b/PhaseAwareNet_SRC/Caffe/readme.docx differ diff --git a/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.cpp b/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.cpp new file mode 100644 index 0000000..facfe99 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.cpp @@ -0,0 +1,76 @@ +#include +#include + +#include "caffe/filler.hpp" +#include "caffe/layer_factory.hpp" +#include "caffe/layers/split_by_phase_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +void SplitByPhaseLayer::LayerSetUp(const vector*>& bottom, + const vector*>& top) { + + num_images_ = bottom[0]->num(); + num_filters_ = bottom[0]->channels(); + height_ = bottom[0]->height(); + width_ = bottom[0]->width(); + + CHECK_EQ(height_, 512); + CHECK_EQ(width_, 512); + +} + +template +void SplitByPhaseLayer::Reshape(const vector*>& bottom, + const vector*>& top) { + + top[0]->Reshape(num_images_, num_filters_*64, 64, 64); + +} + +template +void SplitByPhaseLayer::Forward_cpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->cpu_data(); + Dtype* top_data = top[0]->mutable_cpu_data(); + int n, c, p, h, w, source_index; + for (int index = 0; index < bottom[0]->count(); ++index) { + w = index % 64; + h = (index / 64) % 64; + p = (index / 64 / 64) % 64; + c = (index / 64 / 64 / 64) % num_filters_; + n = index / 64 / 64 / 64 / num_filters_; + source_index = ((w*8)+(h*8*512)+(p%8)+(p/8)*512)+((n*num_filters_+c)*512*512); + top_data[index] = bottom_data[source_index]; + } +} + +template +void SplitByPhaseLayer::Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (propagate_down[0]) { + const Dtype* top_diff = top[0]->cpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_cpu_diff(); + int n, c, p, h, w, source_index; + for (int index = 0; index < bottom[0]->count(); ++index) { + w = index % 64; + h = (index / 64) % 64; + p = (index / 64 / 64) % 64; + c = (index / 64 / 64 / 64) % num_filters_; + n = index / 64 / 64 / 64 / num_filters_; + source_index = ((w*8)+(h*8*512)+(p%8)+(p/8)*512)+((n*num_filters_+c)*512*512); + bottom_diff[source_index] = top_diff[index]; + } + } +} + +#ifdef CPU_ONLY +STUB_GPU(SplitByPhaseLayer); +#endif + +INSTANTIATE_CLASS(SplitByPhaseLayer); +REGISTER_LAYER_CLASS(SplitByPhase); + +} // namespace caffe diff --git a/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.cu b/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.cu new file mode 100644 index 0000000..b460996 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.cu @@ -0,0 +1,92 @@ +#include +#include + +#include "caffe/layers/split_by_phase_layer.hpp" +#include "caffe/util/math_functions.hpp" + +namespace caffe { + +template +__global__ void SplitByPhaseForward(const int nthreads, + const Dtype* const bottom_data, const int num_filters, Dtype* const top_data) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int w = index % 64; + const int h = (index / 64) % 64; + const int p = (index / 64 / 64) % 64; + const int c = (index / 64 / 64 / 64) % num_filters; + const int n = index / 64 / 64 / 64 / num_filters; + const int source_index = ((w*8)+(h*8*512)+(p%8)+(p/8)*512)+((n*num_filters+c)*512*512); + top_data[index] = bottom_data[source_index];; + } +} + +template +__global__ void SplitByPhaseForwardSlow(const int nthreads, + const Dtype* const bottom_data, const int num_filters, Dtype* const top_data) { + CUDA_KERNEL_LOOP(index, nthreads) { + int h, p, c, n, source_index; + for (int w = 0; w < 64; ++w) { + h = index % 64; + p = (index / 64) % 64; + c = (index / 64 / 64) % num_filters; + n = index / 64 / 64 / num_filters; + source_index = ((w*8)+(h*8*512)+(p%8)+(p/8)*512)+((n*num_filters+c)*512*512); + top_data[index*64+w] = bottom_data[source_index]; + } + } +} + +template +void SplitByPhaseLayer::Forward_gpu( + const vector*>& bottom, const vector*>& top) { + const Dtype* bottom_data = bottom[0]->gpu_data(); + Dtype* top_data = top[0]->mutable_gpu_data(); + const int count = top[0]->count(); + // NOLINT_NEXT_LINE(whitespace/operators) + SplitByPhaseForward<<>>( + count, bottom_data, num_filters_, top_data); +} + +template +__global__ void SplitByPhaseBackwardSlow(const int nthreads, + Dtype* const bottom_diff, const int num_filters, const Dtype* const top_diff) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int w = index % 64; + const int h = (index / 64) % 64; + const int p = (index / 64 / 64) % 64; + const int c = (index / 64 / 64 / 64) % num_filters; + const int n = index / 64 / 64 / 64 / num_filters; + const int source_index = ((w*8)+(h*8*512)+(p%8)+(p/8)*512)+((n*num_filters+c)*512*512); + bottom_diff[source_index] = top_diff[index]; + } +} + +template +__global__ void SplitByPhaseBackward(const int nthreads, + Dtype* const bottom_diff, const int num_filters, const Dtype* const top_diff) { + CUDA_KERNEL_LOOP(index, nthreads) { + const int w = index % 512; + const int h = (index / 512) % 512; + const int c = (index / 512 / 512) % num_filters; + const int n = index / 512 / 512 / num_filters; + const int target_index = ((w/8)+64*(h/8))+(64*64*(((w%8)+8*(h%8))))+(512*512*(n*num_filters+c)); + bottom_diff[index] = top_diff[target_index]; + } +} + +template +void SplitByPhaseLayer::Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom) { + if (propagate_down[0]) { + const Dtype* top_diff = top[0]->gpu_diff(); + Dtype* bottom_diff = bottom[0]->mutable_gpu_diff(); + const int count = bottom[0]->count(); + // NOLINT_NEXT_LINE(whitespace/operators) + SplitByPhaseBackward<<>>( + count, bottom_diff, num_filters_, top_diff); + } +} + +INSTANTIATE_LAYER_GPU_FUNCS(SplitByPhaseLayer); + +} // namespace caffe diff --git a/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.hpp b/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.hpp new file mode 100644 index 0000000..7744c79 --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/split_by_phase_layer.hpp @@ -0,0 +1,57 @@ +#ifndef CAFFE_SPLIT_BY_PHASE_LAYER_HPP_ +#define CAFFE_SPLIT_BY_PHASE_LAYER_HPP_ + +#include + +#include "caffe/blob.hpp" +#include "caffe/layer.hpp" +#include "caffe/proto/caffe.pb.h" + +namespace caffe { + +template +class SplitByPhaseLayer: public Layer { + public: + explicit SplitByPhaseLayer(const LayerParameter& param) + : Layer(param) {} + virtual void LayerSetUp(const vector*>& bottom, + const vector*>& top); + virtual void Reshape(const vector*>& bottom, + const vector*>& top); + + virtual inline const char* type() const { return "SplitByPhase"; } + virtual inline int ExactNumBottomBlobs() const { return 1; } + virtual inline int ExactNumTopBlobs() const { return 1; } + + protected: + virtual void Forward_cpu(const vector*>& bottom, + const vector*>& top); + virtual void Forward_gpu(const vector*>& bottom, + const vector*>& top); + virtual void Backward_cpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + virtual void Backward_gpu(const vector*>& top, + const vector& propagate_down, const vector*>& bottom); + + shared_ptr > bias_layer_; + vector*> bias_bottom_vec_; + vector bias_propagate_down_; + int bias_param_id_; + + Blob sum_multiplier_; + Blob sum_result_; + Blob temp_; + int axis_; + int outer_dim_, scale_dim_, inner_dim_; + + int num_images_; + int num_filters_; + int height_; + int width_; + +}; + + +} // namespace caffe + +#endif // CAFFE_SPLIT_BY_PHASE_LAYER_HPP_ diff --git a/PhaseAwareNet_SRC/Caffe/test_split_by_phase_layer.cpp b/PhaseAwareNet_SRC/Caffe/test_split_by_phase_layer.cpp new file mode 100644 index 0000000..78e0b1c --- /dev/null +++ b/PhaseAwareNet_SRC/Caffe/test_split_by_phase_layer.cpp @@ -0,0 +1,123 @@ +#include + +#include "gtest/gtest.h" + +#include "caffe/blob.hpp" +#include "caffe/common.hpp" +#include "caffe/filler.hpp" +#include "caffe/layers/split_by_phase_layer.hpp" + +#include "caffe/test/test_caffe_main.hpp" +#include "caffe/test/test_gradient_check_util.hpp" + +namespace caffe { + +#ifndef CPU_ONLY +extern cudaDeviceProp CAFFE_TEST_CUDA_PROP; +#endif + +template +class SplitByPhaseLayerTest : public MultiDeviceTest { + typedef typename TypeParam::Dtype Dtype; + protected: + SplitByPhaseLayerTest() + : blob_bottom_(new Blob(1, 1, 512, 512)), + blob_bottom_nobatch_(new Blob(1, 2, 3, 4)), + blob_top_(new Blob()) { + // fill the values + FillerParameter filler_param; +// filler_param.s + filler_param.set_min(-9); + filler_param.set_max(9); + UniformFiller filler(filler_param); + filler.Fill(this->blob_bottom_); + blob_top_vec_.push_back(blob_top_); + } + virtual ~SplitByPhaseLayerTest() { + delete blob_bottom_; + delete blob_bottom_nobatch_; + delete blob_top_; + } + Blob* const blob_bottom_; + Blob* const blob_bottom_nobatch_; + Blob* const blob_top_; + vector*> blob_bottom_vec_; + vector*> blob_top_vec_; +}; + +TYPED_TEST_CASE(SplitByPhaseLayerTest, TestDtypesAndDevices); + +TYPED_TEST(SplitByPhaseLayerTest, TestSetUp) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + LayerParameter layer_param; + shared_ptr > layer( + new SplitByPhaseLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + EXPECT_EQ(this->blob_top_vec_[0]->num(), 1); + EXPECT_EQ(this->blob_top_vec_[0]->channels(), 1*64); + EXPECT_EQ(this->blob_top_vec_[0]->height(), 64); + EXPECT_EQ(this->blob_top_vec_[0]->width(), 64); +} + +TYPED_TEST(SplitByPhaseLayerTest, TestForward) { + typedef typename TypeParam::Dtype Dtype; + Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data(); + const int num_filters = this->blob_bottom_->channels(); + int n, c, p, h, w, bottom_fill_idx; + for (int index = 0; index < this->blob_bottom_->count(); ++index) { + w = index % 64; + h = (index / 64) % 64; + p = (index / 64 / 64) % 64; + c = (index / 64 / 64 / 64) % num_filters; + n = index / 64 / 64 / 64 / num_filters; + bottom_fill_idx = ((w*8)+(h*8*512)+(p%8)+(p/8)*512)+((n*num_filters+c)*512*512); + bottom_data[bottom_fill_idx] = p; + } + this->blob_bottom_vec_.push_back(this->blob_bottom_); + bool IS_VALID_CUDA = false; +#ifndef CPU_ONLY + IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; +#endif + if (Caffe::mode() == Caffe::CPU || + sizeof(Dtype) == 4 || IS_VALID_CUDA) { + LayerParameter layer_param; + shared_ptr > layer( + new SplitByPhaseLayer(layer_param)); + layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_); + layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_); + const Dtype* data = this->blob_top_vec_[0]->cpu_data(); + const int num_phase_blocks = this->blob_top_vec_[0]->num()*this->blob_top_vec_[0]->channels(); + for (int nc = 0; nc < num_phase_blocks; ++nc) { + for (int h = 0; h < 64 ; ++h ) { + for (int w = 0; w < 64 ; ++w ) { + CHECK_EQ(data[nc*(64*64)+h*64+w], nc%64); + } + } + } + } else { + LOG(ERROR) << "Skipping test due to old architecture."; + } +} + +TYPED_TEST(SplitByPhaseLayerTest, TestGradient) { + typedef typename TypeParam::Dtype Dtype; + this->blob_bottom_vec_.push_back(this->blob_bottom_); + bool IS_VALID_CUDA = false; +#ifndef CPU_ONLY + IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2; +#endif +// if (Caffe::mode() == Caffe::CPU || +// sizeof(Dtype) == 4 || IS_VALID_CUDA) { + if (Caffe::mode() == Caffe::GPU) { + LayerParameter layer_param; + SplitByPhaseLayer layer(layer_param); + GradientChecker checker(1e-2, 1e-3); + checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, + this->blob_top_vec_); + } else { + LOG(ERROR) << "Skipping test due to old architecture."; + } +} + +} // namespace caffe diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/PhaseAware_PNet_Config.png b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/PhaseAware_PNet_Config.png new file mode 100644 index 0000000..278fce2 Binary files /dev/null and b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/PhaseAware_PNet_Config.png differ diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/PhaseAware_VNet_Config.png b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/PhaseAware_VNet_Config.png new file mode 100644 index 0000000..265da81 Binary files /dev/null and b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/PhaseAware_VNet_Config.png differ diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/bn_refine_phaseaware.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/bn_refine_phaseaware.m new file mode 100644 index 0000000..8d352a2 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/bn_refine_phaseaware.m @@ -0,0 +1,70 @@ +function [BN_Moments, stats] = bn_refine_phaseaware(varargin) +% Test the phasesplit net + +opts.batchSize = 48; +opts.expDir = fullfile('data', 'JUNI-7504-PNet-dagnn-40-Seed-0-log_short') ; +opts.testEpoch = 40; +opts.saveResult = true; +opts.bnEpochCollectSize = 2000; +opts.gpuIdx = 1; + +opts = vl_argparse( opts, varargin ); + +opts.imdbPath = fullfile(opts.expDir, 'imdb.mat'); + +opts.train = struct('gpus', opts.gpuIdx, 'cudnn', true, 'stegoShuffle', true ) ; +if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end; + +% put it to drawing +if ( ~exist( opts.expDir, 'dir' ) ) + error('expDir is empty' ); +end + +% ------------------------------------------------------------------------- +% Find the data base +% ------------------------------------------------------------------------- +if exist(opts.imdbPath, 'file') + imdb = load(opts.imdbPath) ; +else + error(' cannot find imdb' ); +end + + +meta.inputSize = [512, 512, 1, opts.batchSize]; + +[BN_Moments, stats] = cnn_bnrefine_dag(imdb, getBatchFn( opts, meta ), ... + 'expDir', opts.expDir, ... + 'batchSize', opts.batchSize, ... + 'testEpoch', opts.testEpoch, ... + 'bnEpochCollectSize', opts.bnEpochCollectSize, ... + 'saveResult', opts.saveResult, ... + opts.train ) ; + + +% ------------------------------------------------------------------------- +function fn = getBatchFn(opts, meta) +% ------------------------------------------------------------------------- +bopts.useGpu = numel(opts.train.gpus) > 0 ; +bopts.imageSize = meta.inputSize; + +fn = @(x,y) getDagNNBatch(bopts,x,y) ; + +% ------------------------------------------------------------------------- +function inputs = getDagNNBatch(opts, imdb, batch) +% ------------------------------------------------------------------------- +% label +labels = imdb.images.label(1,batch) ; +% images +images = zeros(opts.imageSize(1), opts.imageSize(2), ... + opts.imageSize(3), numel(batch), 'single') ; +for i = 1:numel(batch) + imt = load(imdb.images.name{batch(i)}, 'im'); + images(:,:,:,i) = single(imt.im); +% imt = imread(imdb.images.name{batch(i)}); +% images(:,:,:,i) = single(imt); +end + +if opts.useGpu > 0 + images = gpuArray(images) ; +end +inputs = {'input', images, 'label', labels} ; diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware.m new file mode 100644 index 0000000..1864cd6 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware.m @@ -0,0 +1,149 @@ +function [net, info] = cnn_phaseaware(varargin) +%CNN_PHASEAWARE Demonstrates training a PhaseAwareNet on JUNI and UED + +run(fullfile(fileparts(mfilename('fullpath')), ... + '..', '..', 'matlab', 'vl_setupnn.m')) ; + +opts.modelType = 'PNet'; +opts.seed = 0; +opts.networkType = 'dagnn' ; +opts.batchSize = 40; +opts.lrSequence = 'log_short'; +opts.printDotFile = true; +opts.coverPath = 'C:\DeepLearning\matconvnet-1.0-beta20\data\JStego\75_mat'; +opts.stegoPath = 'C:\DeepLearning\matconvnet-1.0-beta20\data\JStego\JUNI_0.4_mat'; + + +sfx = [opts.modelType, '-', opts.networkType, '-', num2str(opts.batchSize), ... + '-Seed-', num2str(opts.seed), '-', opts.lrSequence] ; +opts.expDir = fullfile('data', ['JUNI-7504-' sfx]) ; % TODO + +opts.imdbPath = fullfile(opts.expDir, 'imdb.mat'); + +opts.train = struct('gpus', [1,2], 'cudnn', true, 'stegoShuffle', true, 'computeBNMoment', true) ; +if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end; + + +% ------------------------------------------------------------------------- +% Prepare model +% ------------------------------------------------------------------------- +if (strcmpi( opts.modelType, 'PNet' )) + + net = cnn_phaseaware_PNet_init( 'networkType', opts.networkType, ... + 'batchSize', opts.batchSize, ... + 'seed', opts.seed, ... + 'lrSequence', opts.lrSequence ); + +elseif (strcmpi( opts.modelType, 'VNet' )) + + net = cnn_phaseaware_VNet_init( 'networkType', opts.networkType, ... + 'batchSize', opts.batchSize, ... + 'seed', opts.seed, ... + 'lrSequence', opts.lrSequence ); + +else + error('Unknown model type'); +end + +% put it to drawing +if ( ~exist( opts.expDir, 'dir' ) ) + mkdir( opts.expDir ); +end + +if opts.printDotFile + net2dot(net, fullfile( opts.expDir, 'NetConfig.dot' ), ... + 'BatchSize', net.meta.trainOpts.batchSize, ... + 'Inputs', {'input', [net.meta.inputSize, net.meta.trainOpts.batchSize]}); +end + +% ------------------------------------------------------------------------- +% Prepare data +% ------------------------------------------------------------------------- +if exist(opts.imdbPath, 'file') + imdb = load(opts.imdbPath) ; +else + imdb = cnn_phaseaware_imdb_setup('coverPath', opts.coverPath, 'stegoPath', opts.stegoPath) ; + + save(opts.imdbPath, '-struct', 'imdb') ; +end + +% Set the class names in the network +net.meta.classes.name = imdb.classes.name ; +net.meta.classes.description = imdb.classes.description ; + + +% ------------------------------------------------------------------------- +% Learn +% ------------------------------------------------------------------------- +switch opts.networkType + case 'dagnn', trainFn = @cnn_train_dag ; + otherwise, error('wrong network type'); +end + +[net, info] = trainFn(net, imdb, getBatchFn(opts, net.meta), ... + 'expDir', opts.expDir, ... + net.meta.trainOpts, ... + opts.train) ; + +modelPath = fullfile(opts.expDir, 'net-deployed.mat'); + +switch opts.networkType + case 'dagnn' + net_ = net.saveobj() ; + save(modelPath, '-struct', 'net_') ; + clear net_ ; +end + +% ------------------------------------------------------------------------- +function fn = getBatchFn(opts, meta) +% ------------------------------------------------------------------------- +bopts.useGpu = numel(opts.train.gpus) > 0 ; +bopts.imageSize = meta.inputSize; + +switch lower(opts.networkType) + case 'dagnn' + fn = @(x,y) getDagNNBatch(bopts,x,y) ; +end + +% ------------------------------------------------------------------------- +function inputs = getDagNNBatch(opts, imdb, batch) +% ------------------------------------------------------------------------- +% label +labels = imdb.images.label(1,batch) ; +% images +images = zeros(opts.imageSize(1), opts.imageSize(2), ... + opts.imageSize(3), numel(batch), 'single') ; + +for i = 1:numel(batch)/2 + +% cover = imread(imdb.images.name{batch(2*i-1)}); +% stego = imread(imdb.images.name{batch(2*i)}); + + imt = load(imdb.images.name{batch(2*i-1)}, 'im'); + cover = single(imt.im); + + imt = load(imdb.images.name{batch(2*i)}, 'im'); + stego = single(imt.im); + + % random rotate, 0, 90, 180, 270 + r = randi(4) - 1; + cover = rot90( cover, r ); + stego = rot90( stego, r ); + + % random mirror flip + if ( rand > 0.5 ) + cover = fliplr( cover ); + stego = fliplr( stego ); + end + + images(:,:,:,2*i-1) = single(cover); + images(:,:,:,2*i) = single(stego); + +end + +if opts.useGpu > 0 + images = gpuArray(images) ; +end +inputs = {'input', images, 'label', labels} ; + + diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_PNet_init.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_PNet_init.m new file mode 100644 index 0000000..dcd9284 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_PNet_init.m @@ -0,0 +1,265 @@ +function net = cnn_phaseaware_PNet_init(varargin) +% Define and initialize PhaseAwareNet net +opts.networkType = 'dagnn' ; +opts.batchSize = 40; +opts.seed = 0; +opts.lrSequence = 'step_long2'; +opts = vl_argparse(opts, varargin) ; + +rng( opts.seed ); + +net.layers = {} ; + +convOpts = {'CudnnWorkspaceLimit', 1024*1024*1204} ; + +HPF = zeros(5, 5, 1, 4, 'single'); + +HPF(:,:,1,1) = [ -1, 2, -2, 2, -1; ... + 2, -6, 8, -6, 2; ... + -2, 8, -12, 8, -2; ... + 2, -6, 8, -6, 2; ... + -1, 2, -2, 2, -1]/12; + +HPF(:,:,1,2) = [ 0, 0, 5.2, 0, 0; ... + 0, 23.4, 36.4, 23.4, 0; ... + 5.2, 36.4, -261.0, 36.4, 5.2; ... + 0, 23.4, 36.4, 23.4, 0; ... + 0, 0, 5.2, 0, 0]/261; + +HPF(:,:,1,3) = [ 0.0562, -0.1354, 0.0000, 0.1354, -0.0562; ... + 0.0818, -0.1970, 0.0000, 0.1970, -0.0818; ... + 0.0926, -0.2233, 0.0000, 0.2233, -0.0926; ... + 0.0818, -0.1970, 0.0000, 0.1970, -0.0818; ... + 0.0562, -0.1354, 0.0000, 0.1354, -0.0562 ]; + +HPF(:,:,1,4) = [-0.0562, -0.0818, -0.0926, -0.0818, -0.0562; ... + 0.1354, 0.1970, 0.2233, 0.1970, 0.1354; ... + 0.0000, 0.0000, 0.0000, -0.0000, -0.0000; ... + -0.1354, -0.1970, -0.2233, -0.1970, -0.1354; ... + 0.0562, 0.0818, 0.0926, 0.0818, 0.0562 ]; + + +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'HPFs', ... + 'weights', {{HPF, []}}, ... + 'learningRate', [0, 0], ... + 'stride', 1, ... + 'pad', 2, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; + +% Group 1 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_1', ... + 'weights', {{init_weight('gaussian', 5, 5, 4, 8, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 2, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'abs', 'name', 'ABS_1') ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_1', ... + 'weights', {{ones(8, 1, 'single'), ... + zeros(8, 1, 'single'), ... + zeros(8, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'tanh', 'name', 'TanH_1') ; + + +% Group 2 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_2', ... + 'weights', {{init_weight('gaussian', 5, 5, 8, 16, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 2, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_2', ... + 'weights', {{ones(16, 1, 'single'), ... + zeros(16, 1, 'single'), ... + zeros(16, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'tanh', 'name', 'TanH_2') ; + + +% Phase split here +net.layers{end+1} = struct('type', 'phasesplit', ... + 'name', 'DCTPhaseSplit', ... + 'pool', [1, 1], ... + 'stride', 8, ... + 'pad', 0 ); + +DCTMode = 64; +% Group 3 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_3', ... + 'weights', {{init_weight('gaussian', 1, 1, 16, 32*DCTMode, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 0, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_3', ... + 'weights', {{ones(32*DCTMode, 1, 'single'), ... + zeros(32*DCTMode, 1, 'single'), ... + zeros(32*DCTMode, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'relu', 'name', 'ReLU_3') ; +net.layers{end+1} = struct('type', 'pool', ... + 'name', 'Pool_3', ... + 'method', 'avg', ... + 'pool', [5 5], ... + 'stride', 2, ... + 'pad', 2) ; + +% Group 4 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_4', ... + 'weights', {{init_weight('gaussian', 1, 1, 32, 64*DCTMode, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 0, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_4', ... + 'weights', {{ones(64*DCTMode, 1, 'single'), ... + zeros(64*DCTMode, 1, 'single'), ... + zeros(64*DCTMode, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'relu', 'name', 'ReLU_4') ; +net.layers{end+1} = struct('type', 'pool', ... + 'name', 'Pool_4', ... + 'method', 'avg', ... + 'pool', [5 5], ... + 'stride', 2, ... + 'pad', 2) ; + +% Group 5 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_5', ... + 'weights', {{init_weight('gaussian', 1, 1, 64, 128*DCTMode, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 0, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_5', ... + 'weights', {{ones(128*DCTMode, 1, 'single'), ... + zeros(128*DCTMode, 1, 'single'), ... + zeros(128*DCTMode, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'relu', 'name', 'ReLU_5') ; +net.layers{end+1} = struct('type', 'pool', ... + 'name', 'Pool_5', ... + 'method', 'avg', ... + 'pool', [16 16], ... + 'stride', 1, ... + 'pad', 0) ; + +% Full connect layer +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'FC',... + 'weights', {{init_weight('xavier', 1,1,128*DCTMode,2, 'single'), ... + 0.01*ones(2, 1, 'single')}}, ... + 'learningRate', [1 2], ... + 'weightDecay', [1 0], ... + 'stride', 1, ... + 'pad', 0) ; + +% Softmax layer +net.layers{end+1} = struct('type', 'softmaxloss', 'name', 'loss') ; + +% Meta parameters +net.meta.inputSize = [512 512 1] ; + +lr = get_lr_sequence(opts.lrSequence); +net.meta.trainOpts.learningRate = lr; +net.meta.trainOpts.numEpochs = numel(lr) ; +net.meta.trainOpts.batchSize = opts.batchSize ; +net.meta.trainOpts.weightDecay = 0.01; + + +% Fill in default values +net = vl_simplenn_tidy(net) ; + +% Switch to DagNN if requested +switch lower(opts.networkType) + case 'simplenn' + % done + case 'dagnn' + net = dagnn.DagNN.fromSimpleNN(net, 'canonicalNames', true) ; + net.addLayer('error', dagnn.Loss('loss', 'classerror'), ... + {'prediction','label'}, 'error') ; + otherwise + assert(false) ; +end + +% ------------------------------------------------------------------------- +function weights = init_weight(weightInitMethod, h, w, in, out, type) +% ------------------------------------------------------------------------- +% See K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into +% rectifiers: Surpassing human-level performance on imagenet +% classification. CoRR, (arXiv:1502.01852v1), 2015. +switch lower(weightInitMethod) + case 'gaussian' + sc = 0.01 ; + weights = randn(h, w, in, out, type)*sc; + case 'xavier' + sc = sqrt(3/(h*w*in)) ; + weights = (rand(h, w, in, out, type)*2 - 1)*sc ; + case 'xavierimproved' + sc = sqrt(2/(h*w*out)) ; + weights = randn(h, w, in, out, type)*sc ; + otherwise + error('Unknown weight initialization method''%s''', weightInitMethod); +end + +function lr = get_lr_sequence( lrGenerationMethod ) + +switch lower(lrGenerationMethod) + case 'step_short' + lr = 0.001 * ones(1, 2); + for i = 1:39 + lr =[lr, lr(end-1:end)*0.9]; + end + case 'log_short' + %lr = logspace(-3, -5, 80); + lr = logspace(-3, -5, 40 ); + case 'step_long' + numInterationPerEpoch = 8000/64; + lrStepSize = 5000/numInterationPerEpoch; % + totalStep = 220000/5000; % CNN is trained for 120,000 iterations + lr = 0.001*ones(1,lrStepSize); + for i = 1:totalStep - 1 + lr = [lr, lr(end-lrStepSize+1:end) *0.9]; + end + case 'step_long2' + numInterationPerEpoch = 8000/64; + lrStepSize = 2500/numInterationPerEpoch; % + totalStep = 12; + lr = 0.001*ones(1,lrStepSize); + for i = 1:totalStep - 1 + lr = [lr, lr(end-lrStepSize+1:end) *0.75]; + end + case 'step_long3' + numInterationPerEpoch = 8000/64; + lrStepSize = 2500/numInterationPerEpoch/2; % + totalStep = 10; + lr = 0.001*ones(1,lrStepSize); + for i = 1:totalStep - 1 + lr = [lr, lr(end-lrStepSize+1:end) *0.5]; + end + otherwise + error('unkown type of lr sequence generation method''%s''', lrGenerationMethod); +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_VNet_init.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_VNet_init.m new file mode 100644 index 0000000..29ea4d0 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_VNet_init.m @@ -0,0 +1,264 @@ +function net = cnn_phaseaware_VNet_init(varargin) +% Define and initialize PhaseAwareNet net +opts.networkType = 'dagnn' ; +opts.batchSize = 40; +opts.seed = 0; +opts.lrSequence = 'step_long2'; +opts = vl_argparse(opts, varargin) ; + +rng( opts.seed ); + +net.layers = {} ; + +convOpts = {'CudnnWorkspaceLimit', 1024*1024*1204} ; + +HPF = zeros(5, 5, 1, 4, 'single'); +HPF(:,:,1,1) = [ -1, 2, -2, 2, -1; ... + 2, -6, 8, -6, 2; ... + -2, 8, -12, 8, -2; ... + 2, -6, 8, -6, 2; ... + -1, 2, -2, 2, -1]/12; + +HPF(:,:,1,2) = [ 0, 0, 5.2, 0, 0; ... + 0, 23.4, 36.4, 23.4, 0; ... + 5.2, 36.4, -261.0, 36.4, 5.2; ... + 0, 23.4, 36.4, 23.4, 0; ... + 0, 0, 5.2, 0, 0]/261; + +HPF(:,:,1,3) = [ 0.0562, -0.1354, 0.0000, 0.1354, -0.0562; ... + 0.0818, -0.1970, 0.0000, 0.1970, -0.0818; ... + 0.0926, -0.2233, 0.0000, 0.2233, -0.0926; ... + 0.0818, -0.1970, 0.0000, 0.1970, -0.0818; ... + 0.0562, -0.1354, 0.0000, 0.1354, -0.0562 ]; + +HPF(:,:,1,4) = [-0.0562, -0.0818, -0.0926, -0.0818, -0.0562; ... + 0.1354, 0.1970, 0.2233, 0.1970, 0.1354; ... + 0.0000, 0.0000, 0.0000, -0.0000, -0.0000; ... + -0.1354, -0.1970, -0.2233, -0.1970, -0.1354; ... + 0.0562, 0.0818, 0.0926, 0.0818, 0.0562 ]; + + +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'HPFs', ... + 'weights', {{HPF, []}}, ... + 'learningRate', [0, 0], ... + 'stride', 1, ... + 'pad', 2, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; + +% Group 1 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_1', ... + 'weights', {{init_weight('gaussian', 5, 5, 4, 8, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 2, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'abs', 'name', 'ABS_1') ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_1', ... + 'weights', {{ones(8, 1, 'single'), ... + zeros(8, 1, 'single'), ... + zeros(8, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'tanh', 'name', 'TanH_1') ; + + +% Group 2 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_2', ... + 'weights', {{init_weight('gaussian', 5, 5, 8, 16, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 2, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_2', ... + 'weights', {{ones(16, 1, 'single'), ... + zeros(16, 1, 'single'), ... + zeros(16, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'tanh', 'name', 'TanH_2') ; + + +% Phase split here +net.layers{end+1} = struct('type', 'phasesplit', ... + 'name', 'DCTPhaseSplit', ... + 'pool', [1, 1], ... + 'stride', 8, ... + 'pad', 0 ); + +DCTMode = 64; +% Group 3 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_3', ... + 'weights', {{init_weight('gaussian', 1, 1, 16*DCTMode, 128, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 0, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_3', ... + 'weights', {{ones(128, 1, 'single'), ... + zeros(128, 1, 'single'), ... + zeros(128, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'relu', 'name', 'ReLU_3') ; +net.layers{end+1} = struct('type', 'pool', ... + 'name', 'Pool_3', ... + 'method', 'avg', ... + 'pool', [5 5], ... + 'stride', 2, ... + 'pad', 2) ; + +% Group 4 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_4', ... + 'weights', {{init_weight('gaussian', 1, 1, 128, 256, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 0, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_4', ... + 'weights', {{ones(256, 1, 'single'), ... + zeros(256, 1, 'single'), ... + zeros(256, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'relu', 'name', 'ReLU_4') ; +net.layers{end+1} = struct('type', 'pool', ... + 'name', 'Pool_4', ... + 'method', 'avg', ... + 'pool', [5 5], ... + 'stride', 2, ... + 'pad', 2) ; + +% Group 5 +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'CONV_5', ... + 'weights', {{init_weight('gaussian', 1, 1, 256, 512, 'single'), ... + []}}, ... + 'learningRate', [1, 0], ... + 'stride', 1, ... + 'pad', 0, ... + 'weightDecay', [0, 0], ... + 'opts', {convOpts}) ; +net.layers{end+1} = struct('type', 'bnorm', 'name', 'BN_5', ... + 'weights', {{ones(512, 1, 'single'), ... + zeros(512, 1, 'single'), ... + zeros(512, 2, 'single')}}, ... + 'learningRate', [1 1 0.01], ... + 'weightDecay', [0 0]) ; +net.layers{end+1} = struct('type', 'relu', 'name', 'ReLU_5') ; +net.layers{end+1} = struct('type', 'pool', ... + 'name', 'Pool_5', ... + 'method', 'avg', ... + 'pool', [16 16], ... + 'stride', 1, ... + 'pad', 0) ; + +% Full connect layer +net.layers{end+1} = struct('type', 'conv', ... + 'name', 'FC',... + 'weights', {{init_weight('xavier', 1,1,512,2, 'single'), ... + 0.01*ones(2, 1, 'single')}}, ... + 'learningRate', [1 2], ... + 'weightDecay', [1 0], ... + 'stride', 1, ... + 'pad', 0) ; + +% Softmax layer +net.layers{end+1} = struct('type', 'softmaxloss', 'name', 'loss') ; + +% Meta parameters +net.meta.inputSize = [512 512 1] ; + +lr = get_lr_sequence(opts.lrSequence); +net.meta.trainOpts.learningRate = lr; +net.meta.trainOpts.numEpochs = numel(lr) ; +net.meta.trainOpts.batchSize = opts.batchSize ; +net.meta.trainOpts.weightDecay = 0.01; % In the paper it is 0.01, + %but it is only applied to Batch Normalization + +% Fill in default values +net = vl_simplenn_tidy(net) ; + +% Switch to DagNN if requested +switch lower(opts.networkType) + case 'simplenn' + % done + case 'dagnn' + net = dagnn.DagNN.fromSimpleNN(net, 'canonicalNames', true) ; + net.addLayer('error', dagnn.Loss('loss', 'classerror'), ... + {'prediction','label'}, 'error') ; + otherwise + assert(false) ; +end + +% ------------------------------------------------------------------------- +function weights = init_weight(weightInitMethod, h, w, in, out, type) +% ------------------------------------------------------------------------- +% See K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into +% rectifiers: Surpassing human-level performance on imagenet +% classification. CoRR, (arXiv:1502.01852v1), 2015. +switch lower(weightInitMethod) + case 'gaussian' + sc = 0.01 ; + weights = randn(h, w, in, out, type)*sc; + case 'xavier' + sc = sqrt(3/(h*w*in)) ; + weights = (rand(h, w, in, out, type)*2 - 1)*sc ; + case 'xavierimproved' + sc = sqrt(2/(h*w*out)) ; + weights = randn(h, w, in, out, type)*sc ; + otherwise + error('Unknown weight initialization method''%s''', weightInitMethod); +end + +function lr = get_lr_sequence( lrGenerationMethod ) + +switch lower(lrGenerationMethod) + case 'step_short' + lr = 0.001 * ones(1, 2); + for i = 1:39 + lr =[lr, lr(end-1:end)*0.9]; + end + case 'log_short' + %lr = logspace(-3, -5, 80); + lr = logspace(-3, -5, 40 ); + case 'step_long' + numInterationPerEpoch = 8000/64; + lrStepSize = 5000/numInterationPerEpoch; % + totalStep = 220000/5000; % CNN is trained for 120,000 iterations + lr = 0.001*ones(1,lrStepSize); + for i = 1:totalStep - 1 + lr = [lr, lr(end-lrStepSize+1:end) *0.9]; + end + case 'step_long2' + numInterationPerEpoch = 8000/64; + lrStepSize = 2500/numInterationPerEpoch; % + totalStep = 12; % 8: 160 epoch 12: 240 epoch + lr = 0.001*ones(1,lrStepSize); + for i = 1:totalStep - 1 + lr = [lr, lr(end-lrStepSize+1:end) *0.75]; + end + case 'step_long3' + numInterationPerEpoch = 8000/64; + lrStepSize = 2500/numInterationPerEpoch/2; % + totalStep = 12; + lr = 0.001*ones(1,lrStepSize); + for i = 1:totalStep - 1 + lr = [lr, lr(end-lrStepSize+1:end) *0.5]; + end + otherwise + error('unkown type of lr sequence generation method''%s''', lrGenerationMethod); +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_imdb_setup.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_imdb_setup.m new file mode 100644 index 0000000..c05e8fe --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/cnn_phaseaware_imdb_setup.m @@ -0,0 +1,149 @@ +function imdb = cnn_phaseaware_imdb_setup( varargin ) + +opts.seed = 0; +opts.coverPath = 'C:\DeepLearning\matconvnet-1.0-beta20\data\JStego\75_mat'; +opts.stegoPath = 'C:\DeepLearning\matconvnet-1.0-beta20\data\JStego\JUNI_0.4_mat'; +opts.ratio = [0.6, 0.15, 0.25]; % train, validation, and test +opts.libSize = inf; +opts = vl_argparse( opts, varargin ); + +rng( opts.seed ); +opts.ratio = opts.ratio/sum(opts.ratio); + +% ------------------------------------------------------------------------- +% Sanity Check +% ------------------------------------------------------------------------- +fprintf('sanity check the library images ...') ; +targetSize = 10000; +expArray = linspace(1, targetSize, targetSize); + +% first, sanilty the two data base +list = dir(fullfile(opts.coverPath, '*.mat')); +tokens = regexp({list.name}, '([\d]+).mat', 'tokens') ; +nameArray = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ; +if( ~isequal( sort(nameArray), expArray ) ) + error('coverPath = %s is corrupted', opts.coverPath); +end + +list = dir(fullfile(opts.stegoPath, '*.mat')); +tokens = regexp({list.name}, '([\d]+).mat', 'tokens') ; +nameArray = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ; +if( ~isequal( sort(nameArray), expArray ) ) + error('stegoPath = %s is corrupted', opts.stegoPath); +end +fprintf('[checked]\n') ; + +% meta +randomImages = randperm( targetSize ); + +totalSize = min( opts.libSize, targetSize ); + +numTrn = fix( totalSize * opts.ratio(1)); +numVal = fix( totalSize * opts.ratio(2)); +numTst = fix( totalSize * opts.ratio(3)); + +imdb.classes.name = {'Cover', 'Stego'} ; +n = strfind(opts.coverPath, filesep); +if( isempty( n ) ) + coverDes = 'Cover Images'; +else + coverDes = opts.coverPath(n(end)+1:end); +end + +n = strfind(opts.stegoPath, filesep); +if( isempty( n ) ) + stegoDes = 'Stego Images'; +else + stegoDes = opts.stegoPath(n(end)+1:end); +end + +imdb.classes.description = {coverDes, stegoDes} ; +imdb.classes.coverPath = opts.coverPath; +imdb.classes.stegoPath = opts.stegoPath; + +fprintf('%d Trn Image, %d Val Images, and %d Test Images \n ', ... + numTrn, numVal, numTst) ; + +% ------------------------------------------------------------------------- +% Training images +% ------------------------------------------------------------------------- +fprintf('searching training images ...') ; + +names = cell(1, numTrn * 2 ); +labels = ones(1, numTrn * 2 ); +for i = 1:numTrn + + idx = randomImages(i); + + names{2*i-1} = fullfile(opts.coverPath, strcat(num2str(idx),'.mat')); + labels(2*i - 1) = 1; + + names{2*i} = fullfile(opts.stegoPath, strcat(num2str(idx),'.mat')); + labels(2*i) = 2; +end + +imdb.images.id = 1:numel(names) ; +imdb.images.name = names ; +imdb.images.set = ones(1, numel(names)) ; +imdb.images.label = labels ; + +fprintf('done\n') ; +% ------------------------------------------------------------------------- +% Validation images +% ------------------------------------------------------------------------- +fprintf('searching validation images ...') ; + +names = cell(1, numVal * 2 ); +labels = ones(1, numVal * 2 ); + +for i = 1:numVal + + idx = randomImages( numTrn + i); + + names{2*i-1} = fullfile(opts.coverPath, strcat(num2str(idx),'.mat')); + labels(2*i - 1) = 1; + + names{2*i} = fullfile(opts.stegoPath, strcat(num2str(idx),'.mat')); + labels(2*i) = 2; + +end + +imdb.images.id = horzcat( imdb.images.id, (1:numel(names)) + 1e7 - 1 ); +imdb.images.name = horzcat(imdb.images.name, names ); +imdb.images.set = horzcat( imdb.images.set, 2 * ones(1, numel(names))); +imdb.images.label = horzcat( imdb.images.label, labels ) ; + +fprintf('done\n') ; + +% ------------------------------------------------------------------------- +% Test images +% ------------------------------------------------------------------------- + +fprintf('searching test images ...') ; + +names = cell(1, numTst * 2 ); +labels = ones(1, numTst * 2 ); + +for i = 1:numTst + + idx = randomImages( numTrn + numVal + i); + + names{2*i-1} = fullfile(opts.coverPath, strcat(num2str(idx),'.mat')); + labels(2*i - 1) = 1; + + names{2*i} = fullfile(opts.stegoPath, strcat(num2str(idx),'.mat')); + labels(2*i) = 2; + +end + +imdb.images.id = horzcat( imdb.images.id, (1:numel(names)) + 2e7 - 1 ); +imdb.images.name = horzcat(imdb.images.name, names ); +imdb.images.set = horzcat( imdb.images.set, 3 * ones(1, numel(names))); +imdb.images.label = horzcat( imdb.images.label, labels ) ; + +fprintf('done\n') ; + + + + + diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/test_phaseaware.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/test_phaseaware.m new file mode 100644 index 0000000..ba69e5d --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/PhaseAwareNet/test_phaseaware.m @@ -0,0 +1,72 @@ +function [stats, state] = test_phaseaware(varargin) +% Test the phasesplit net + +opts.batchSize = 40; +opts.expDir = fullfile('data', 'JUNI-7504-PNet-dagnn-40-Seed-0-log_short') ; +opts.testEpoch = 40; +opts.testSelect = [0, 1, 1]; % (1) training; (2)validation; (3), testing +opts.saveResult = true; +opts.bnRefine = true; + +opts = vl_argparse( opts, varargin ); + +opts.imdbPath = fullfile(opts.expDir, 'imdb.mat'); + +opts.train = struct('gpus', [1, 2], 'cudnn', true, 'stegoShuffle', false ) ; +%opts.train = struct('gpus', [], 'stegoShuffle', true) ; // CPU debugging +if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end; + +% put it to drawing +if ( ~exist( opts.expDir, 'dir' ) ) + error('expDir is empty' ); +end + +% ------------------------------------------------------------------------- +% Find the data base +% ------------------------------------------------------------------------- +if exist(opts.imdbPath, 'file') + imdb = load(opts.imdbPath) ; +else + error(' cannot find imdb' ); +end + +meta.inputSize = [512, 512, 1, opts.batchSize]; + +[state, stats] = cnn_test_dag(imdb, getBatchFn( opts, meta ), ... + 'expDir', opts.expDir, ... + 'batchSize', opts.batchSize, ... + 'testEpoch', opts.testEpoch, ... + 'testSelect', opts.testSelect, ... + 'saveResult', opts.saveResult, ... + 'bnRefine', opts.bnRefine, ... + opts.train ) ; + + +% ------------------------------------------------------------------------- +function fn = getBatchFn(opts, meta) +% ------------------------------------------------------------------------- +bopts.useGpu = numel(opts.train.gpus) > 0 ; +bopts.imageSize = meta.inputSize; + +fn = @(x,y) getDagNNBatch(bopts,x,y) ; + + +% ------------------------------------------------------------------------- +function inputs = getDagNNBatch(opts, imdb, batch) +% ------------------------------------------------------------------------- +% label +labels = imdb.images.label(1,batch) ; +% images +images = zeros(opts.imageSize(1), opts.imageSize(2), ... + opts.imageSize(3), numel(batch), 'single') ; +for i = 1:numel(batch) +% imt = imread(imdb.images.name{batch(i)}); +% images(:,:,:,i) = single(imt); + imt = load(imdb.images.name{batch(i)}, 'im'); + images(:,:,:,i) = single(imt.im); +end + +if opts.useGpu > 0 + images = gpuArray(images) ; +end +inputs = {'input', images, 'label', labels} ; diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_bnrefine_dag.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_bnrefine_dag.m new file mode 100644 index 0000000..8f9bf27 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_bnrefine_dag.m @@ -0,0 +1,284 @@ +function [BN_Moments,stats] = cnn_bnrefine_dag( imdb, getBatch, varargin) +%CNN_TEST_DAG Demonstrates test a CNN using the DagNN wrapper +% CNN_TEST_DAG() is a slim version to CNN_TRAIN_DAG(), just do the +% testing of the final net in the export + +opts.expDir = fullfile('data','exp') ; +opts.batchSize = 256 ; +opts.train = [] ; +opts.val = [] ; +opts.test = []; +opts.gpus = [] ; +opts.prefetch = false ; +opts.testEpoch = inf; +opts.bnEpochCollectSize = 2000; +opts.saveResult = true; + +opts.randomSeed = 0 ; +opts.stegoShuffle = false; +opts.cudnn = true ; +opts.extractStatsFn = @extractStats ; + +opts = vl_argparse(opts, varargin) ; + +if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end +if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end +if isnan(opts.train), opts.train = [] ; end + +% we must restrict the BN moment pooling from train set only + +% ------------------------------------------------------------------------- +% Initialization +% ------------------------------------------------------------------------- + +state.getBatch = getBatch ; + +% ------------------------------------------------------------------------- +% Train and validate +% ------------------------------------------------------------------------- + +modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep)); +resultPath = @(ep) fullfile(opts.expDir, sprintf('bn-epoch-%d.mat', ep)); + +start = findLastCheckpoint(opts.expDir) ; +if( start < 1 ) + error( 'Found no net' ); +end + +if start >= 1 + start = min(start, opts.testEpoch); + fprintf('%s: testing by loading epoch %d\n', mfilename, start) ; + net = loadState(modelPath(start)) ; +end + +% First, create the structure to pool the BN moments +numLayers = numel(net.layers); + +BN_Moments = struct('layer', {}, ... + 'name', {}, ... + 'inputs', {}, ... + 'outputs', {}, ... + 'shape', {}, ... + 'dataType', {}, ... + 'oldValue', {}, ... + 'hist', {} ) ; + +for i = 1:numLayers + if ( isa( net.layers(i).block, 'dagnn.BatchNorm') ) + % Neet to save the BN moments for pooling + net.layers(i).block.computeMoment = true; + + name = net.layers(i).params{3}; + dataType = class(net.getParam(name).value); + shape = size(net.getParam(name).value); + + BN_Moments(end+1).layer = net.layers(i).name; + BN_Moments(end).name = name ; + BN_Moments(end).inputs = net.layers(i).inputs; + BN_Moments(end).outputs = net.layers(i).outputs; + BN_Moments(end).shape = shape ; + BN_Moments(end).dataType = dataType ; + BN_Moments(end).oldValue = net.getParam(name).value; + end +end + + +if( numel(opts.gpus) > 1 ) + error( 'cannot support multiple GPU now ') +end + +numEpoch = ceil(opts.bnEpochCollectSize/(numel(opts.train)/opts.batchSize)); + +rng(start + opts.randomSeed) ; + +for epoch = start:start + numEpoch - 1 + + % Set the random seed based on the epoch and opts.randomSeed. + % This is important for reproducibility, including when training + % is restarted from a checkpoint. + + prepareGPUs( opts, true ) ; + + % Train for one epoch. + state.epoch = epoch ; + + % shuffle + if( opts.stegoShuffle ) + + N = numel(opts.train); % TRN + + Lab = max( 1, numel(opts.gpus)); + + % M and N must be even, and multiple Lab + assert( rem( N, 2*Lab ) == 0 ); + + seq = opts.train( 2*randperm(N/2) - 1 ); + seq = reshape( seq, Lab, N/(2*Lab) ); + state.train = reshape( [seq; seq+1], 1, N ); + + else + + state.train = opts.train(randperm(numel(opts.train))) ; + + end + + state.imdb = imdb ; + + % keep pooling the result + [stats.train(epoch - start + 1), BN_Moments] = process_epoch(net, state, opts, BN_Moments ) ; + +end + +% Reset the parameters +for i = 1:numel(BN_Moments) + bn_moment_name = BN_Moments(i).name; + statsVal = median(BN_Moments(i).hist, 3); + + % set the new value + paramIdx = net.getParamIndex(bn_moment_name); + % double check the shape, see if it matches + assert( isequal(size(statsVal), size(net.params(paramIdx).value ) ) ); + + % reset the BN moment parameters + net.params(paramIdx).value = statsVal; +end + +% Revert it back +for i = 1:numel(net.layers) + if ( isa( net.layers(i).block, 'dagnn.BatchNorm') ) + net.layers(i).block.computeMoment = false; + end +end + + +saveState(resultPath(start), net, stats, BN_Moments ) ; + +% ------------------------------------------------------------------------- +function [stats, BN_Moments] = process_epoch(net, state, opts, BN_Moments ) +% ------------------------------------------------------------------------- + +% move CNN to GPU as needed +numGpus = numel(opts.gpus) ; +if numGpus >= 1 + net.move('gpu') ; +end + +subset = state.train; +num = 0 ; +stats.num = 0 ; % return something even if subset = [] +stats.time = 0 ; +adjustTime = 0 ; + +start = tic ; +for t=1:opts.batchSize:numel(subset) + fprintf('%s: epoch %02d: %3d/%3d:', 'test', state.epoch, ... + fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize)) ; + batchSize = min(opts.batchSize, numel(subset) - t + 1) ; + + % get this image batch and prefetch the next + s = 1; + batchStart = t + (labindex-1) + (s-1) * numlabs ; + batchEnd = min(t+opts.batchSize-1, numel(subset)) ; + batch = subset(batchStart : numlabs : batchEnd) ; + num = num + numel(batch) ; + if numel(batch) == 0, continue ; end + + inputs = state.getBatch(state.imdb, batch) ; + + net.mode = 'test' ; + + net.eval(inputs) ; + + % update here + for i = 1:numel(BN_Moments) + layer_name = BN_Moments(i).layer; + newVal = gather( net.getLayer(layer_name).block.moments ); + assert( ~isempty( newVal ) ); % in case the BatchNorm is not set up + BN_Moments(i).hist = cat( 3, BN_Moments(i).hist, newVal ); + end + + + % get statistics + time = toc(start) + adjustTime ; + batchTime = time - stats.time ; + stats = opts.extractStatsFn(net) ; + stats.num = num ; + stats.time = time ; + currentSpeed = batchSize / batchTime ; + averageSpeed = (t + batchSize - 1) / time ; + if t == opts.batchSize + 1 + % compensate for the first iteration, which is an outlier + adjustTime = 2*batchTime - time ; + stats.time = time + adjustTime ; + end + + fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ; + for f = setdiff(fieldnames(stats)', {'num', 'time'}) + f = char(f) ; + fprintf(' %s:', f) ; + fprintf(' %.3f', stats.(f)) ; + end + fprintf('\n') ; +end + +net.reset() ; +net.move('cpu') ; + + +% ------------------------------------------------------------------------- +function stats = extractStats(net) +% ------------------------------------------------------------------------- +sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ; +stats = struct() ; +for i = 1:numel(sel) + stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ; +end + +% ------------------------------------------------------------------------- +function saveState(fileName, net, stats, BN_Moments ) +% ------------------------------------------------------------------------- +net_ = net ; +net = net_.saveobj() ; +save(fileName, 'net', 'stats', 'BN_Moments') ; + +% ------------------------------------------------------------------------- +function net = loadState(fileName) +% ------------------------------------------------------------------------- +load(fileName, 'net' ) ; +net = dagnn.DagNN.loadobj(net) ; + +% ------------------------------------------------------------------------- +function epoch = findLastCheckpoint(modelDir) +% ------------------------------------------------------------------------- +list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ; +tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ; +epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ; +epoch = max([epoch 0]) ; + + +% ------------------------------------------------------------------------- +function prepareGPUs(opts, cold) +% ------------------------------------------------------------------------- +numGpus = numel(opts.gpus) ; +if numGpus > 1 + % check parallel pool integrity as it could have timed out + pool = gcp('nocreate') ; + if ~isempty(pool) && pool.NumWorkers ~= numGpus + delete(pool) ; + end + pool = gcp('nocreate') ; + if isempty(pool) + parpool('local', numGpus) ; + cold = true ; + end +end +if numGpus >= 1 && cold + fprintf('%s: resetting GPU\n', mfilename) + if numGpus == 1 + gpuDevice(opts.gpus) + else + spmd, gpuDevice(opts.gpus(labindex)), end + end +end + +%end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_test_dag.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_test_dag.m new file mode 100644 index 0000000..b457b23 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_test_dag.m @@ -0,0 +1,341 @@ +function [state,stats] = cnn_test_dag( imdb, getBatch, varargin) +%CNN_TEST_DAG Demonstrates test a CNN using the DagNN wrapper +% CNN_TEST_DAG() is a slim version to CNN_TRAIN_DAG(), just do the +% testing of the final net in the export + +opts.expDir = fullfile('data','exp') ; +opts.batchSize = 256 ; +opts.numSubBatches = 1 ; +opts.train = [] ; +opts.val = [] ; +opts.test = []; +opts.gpus = [] ; +opts.prefetch = false ; +opts.testEpoch = inf; +opts.testSelect = [1, 1, 1]; % (1) training; (2)validation; (3), testing +opts.saveResult = true; +opts.bnRefine = false; + +opts.randomSeed = 0 ; +opts.stegoShuffle = false; +opts.cudnn = true ; +opts.extractStatsFn = @extractStats ; + +opts = vl_argparse(opts, varargin) ; + +if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end +if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end +if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end +if isempty(opts.test), opts.test = find(imdb.images.set==3); end +if isnan(opts.train), opts.train = [] ; end + +% ------------------------------------------------------------------------- +% Initialization +% ------------------------------------------------------------------------- + +state.getBatch = getBatch ; + +% ------------------------------------------------------------------------- +% Train and validate +% ------------------------------------------------------------------------- + +if ( opts.bnRefine ) + modelPath = @(ep) fullfile(opts.expDir, sprintf('bn-epoch-%d.mat', ep)); + resultPath = @(ep) fullfile(opts.expDir, sprintf('test-bn-epoch-%d.mat', ep)); +else + modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep)); + resultPath = @(ep) fullfile(opts.expDir, sprintf('test-net-epoch-%d.mat', ep)); +end + +start = findLastCheckpoint(opts.expDir) ; +if( start < 1 ) + error( 'Found no net' ); +end + +if start >= 1 + start = min(start, opts.testEpoch); + fprintf('%s: testing by loading epoch name %s\n', mfilename, modelPath(start) ); + net = loadState(modelPath(start)) ; +end + +% Make sure that we use the estimated BN moments +for i = 1:numel(net.layers) + if ( isa( net.layers(i).block, 'dagnn.BatchNorm') ) + net.layers(i).block.computeMoment = false; + end +end + + +for epoch = start + + % Set the random seed based on the epoch and opts.randomSeed. + % This is important for reproducibility, including when training + % is restarted from a checkpoint. + + rng(epoch + opts.randomSeed) ; + prepareGPUs(opts, true ) ; + + % Train for one epoch. + state.epoch = epoch ; + + % shuffle + if( opts.stegoShuffle ) + + N = numel(opts.train); % TRN + M = numel(opts.val); % VAL + K = numel(opts.test); % TST + + Lab = max( 1, numel(opts.gpus)); + + % M and N must be even, and multiple Lab + assert( ( rem( N, 2*Lab ) == 0 ) & ... + ( rem( M, 2*Lab ) == 0 ) & ... + ( rem( K, 2*Lab ) == 0 ) ); + + seq = opts.train( 2*randperm(N/2) - 1 ); + seq = reshape( seq, Lab, N/(2*Lab) ); + state.train = reshape( [seq; seq+1], 1, N ); + + seq = opts.val( 2*randperm(M/2) - 1 ); + seq = reshape( seq, Lab, M/(2*Lab) ); + state.val = reshape( [seq; seq+1], 1, M ); + + seq = opts.test( 2*randperm(K/2) - 1 ); + seq = reshape( seq, Lab, K/(2*Lab) ); + state.test = reshape( [seq; seq+1], 1, K ); + + else + + state.train = opts.train(randperm(numel(opts.train))) ; + state.val = opts.val(randperm(numel(opts.val))) ; + state.test = opts.test(randperm(numel(opts.test))) ; + +% N = numel(opts.train); % TRN +% M = numel(opts.val); % VAL +% K = numel(opts.test); % TST +% +% +% state.train = opts.train([1:2:N, 2:2:N]); +% state.val = opts.val([1:2:M, 2:2:M]); +% state.test = opts.test([1:2:K, 2:2:K]); + + end + + state.imdb = imdb ; + + if numel(opts.gpus) <= 1 + if( opts.testSelect(1) ) + stats.train = process_epoch(net, state, opts, 'train') ; + end + if( opts.testSelect(2) ) + stats.val = process_epoch(net, state, opts, 'val') ; + end + if( opts.testSelect(3) ) + stats.test = process_epoch(net, state, opts, 'test'); + end + + else + savedNet = net.saveobj() ; + spmd + net_ = dagnn.DagNN.loadobj(savedNet) ; + if( opts.testSelect(1) ) + stats_.train = process_epoch(net_, state, opts, 'train') ; + end + if( opts.testSelect(2) ) + stats_.val = process_epoch(net_, state, opts, 'val') ; + end + if( opts.testSelect(3) ) + stats_.test = process_epoch(net_, state, opts, 'test'); + end + if labindex == 1, savedNet_ = net_.saveobj() ; end + end + net = dagnn.DagNN.loadobj(savedNet_{1}) ; + stats__ = accumulateStats(stats_) ; + + if( opts.testSelect(1) ) + stats.train = stats__.train ; + end + if( opts.testSelect(2) ) + stats.val = stats__.val ; + end + if( opts.testSelect(3) ) + stats.test = stats__.test; + end + + clear net_ stats_ stats__ savedNet savedNet_ ; + end + + % save + if( opts.saveResult == true ) + saveState(resultPath(epoch), net, stats, state) ; + end + +end + +% ------------------------------------------------------------------------- +function stats = process_epoch(net, state, opts, mode) +% ------------------------------------------------------------------------- + +% move CNN to GPU as needed +numGpus = numel(opts.gpus) ; +if numGpus >= 1 + net.move('gpu') ; +end + +subset = state.(mode) ; +num = 0 ; +stats.num = 0 ; % return something even if subset = [] +stats.time = 0 ; +adjustTime = 0 ; + +start = tic ; +for t=1:opts.batchSize:numel(subset) + fprintf('%s: epoch %02d: %3d/%3d:', mode, state.epoch, ... + fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize)) ; + batchSize = min(opts.batchSize, numel(subset) - t + 1) ; + + for s=1:opts.numSubBatches + % get this image batch and prefetch the next + batchStart = t + (labindex-1) + (s-1) * numlabs ; + batchEnd = min(t+opts.batchSize-1, numel(subset)) ; + batch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ; + num = num + numel(batch) ; + if numel(batch) == 0, continue ; end + + inputs = state.getBatch(state.imdb, batch) ; + + if opts.prefetch + if s == opts.numSubBatches + batchStart = t + (labindex-1) + opts.batchSize ; + batchEnd = min(t+2*opts.batchSize-1, numel(subset)) ; + else + batchStart = batchStart + numlabs ; + end + nextBatch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ; + state.getBatch(state.imdb, nextBatch) ; + end + + net.mode = 'test' ; + net.eval(inputs) ; + end + + % get statistics + time = toc(start) + adjustTime ; + batchTime = time - stats.time ; + stats = opts.extractStatsFn(net) ; + stats.num = num ; + stats.time = time ; + currentSpeed = batchSize / batchTime ; + averageSpeed = (t + batchSize - 1) / time ; + if t == opts.batchSize + 1 + % compensate for the first iteration, which is an outlier + adjustTime = 2*batchTime - time ; + stats.time = time + adjustTime ; + end + + fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ; + for f = setdiff(fieldnames(stats)', {'num', 'time'}) + f = char(f) ; + fprintf(' %s:', f) ; + fprintf(' %.3f', stats.(f)) ; + end + fprintf('\n') ; +end + +net.reset() ; +net.move('cpu') ; + + +% ------------------------------------------------------------------------- +function stats = accumulateStats(stats_) +% ------------------------------------------------------------------------- + +for s = {'train', 'val', 'test'} + s = char(s) ; + total = 0 ; + + % initialize stats stucture with same fields and same order as + % stats_{1} + stats__ = stats_{1} ; + if ( ~isfield(stats__, s) ) + continue; + end + names = fieldnames(stats__.(s))' ; + values = zeros(1, numel(names)) ; + fields = cat(1, names, num2cell(values)) ; + stats.(s) = struct(fields{:}) ; + + for g = 1:numel(stats_) + stats__ = stats_{g} ; + num__ = stats__.(s).num ; + total = total + num__ ; + + for f = setdiff(fieldnames(stats__.(s))', 'num') + f = char(f) ; + stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ; + + if g == numel(stats_) + stats.(s).(f) = stats.(s).(f) / total ; + end + end + end + stats.(s).num = total ; +end + +% ------------------------------------------------------------------------- +function stats = extractStats(net) +% ------------------------------------------------------------------------- +sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ; +stats = struct() ; +for i = 1:numel(sel) + stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ; +end + +% ------------------------------------------------------------------------- +function saveState(fileName, net, stats, state ) +% ------------------------------------------------------------------------- +net_ = net ; +net = net_.saveobj() ; +save(fileName, 'net', 'stats', 'state') ; + +% ------------------------------------------------------------------------- +function [net, stats] = loadState(fileName) +% ------------------------------------------------------------------------- +load(fileName, 'net', 'stats') ; +net = dagnn.DagNN.loadobj(net) ; + +% ------------------------------------------------------------------------- +function epoch = findLastCheckpoint(modelDir) +% ------------------------------------------------------------------------- +list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ; +tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ; +epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ; +epoch = max([epoch 0]) ; + + +% ------------------------------------------------------------------------- +function prepareGPUs(opts, cold) +% ------------------------------------------------------------------------- +numGpus = numel(opts.gpus) ; +if numGpus > 1 + % check parallel pool integrity as it could have timed out + pool = gcp('nocreate') ; + if ~isempty(pool) && pool.NumWorkers ~= numGpus + delete(pool) ; + end + pool = gcp('nocreate') ; + if isempty(pool) + parpool('local', numGpus) ; + cold = true ; + end +end +if numGpus >= 1 && cold + fprintf('%s: resetting GPU\n', mfilename) + if numGpus == 1 + gpuDevice(opts.gpus) + else + spmd, gpuDevice(opts.gpus(labindex)), end + end +end + +%end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_train_dag.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_train_dag.m new file mode 100644 index 0000000..21b71c3 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/examples/cnn_train_dag.m @@ -0,0 +1,516 @@ +function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin) +%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper +% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with +% the DagNN wrapper instead of the SimpleNN wrapper. + +% Copyright (C) 2014-16 Andrea Vedaldi. +% All rights reserved. +% +% This file is part of the VLFeat library and is made available under +% the terms of the BSD license (see the COPYING file). + +opts.expDir = fullfile('data','exp') ; +opts.continue = true ; +opts.batchSize = 256 ; +opts.numSubBatches = 1 ; +opts.train = [] ; +opts.val = [] ; +opts.gpus = [] ; +opts.prefetch = false ; +opts.numEpochs = 300 ; +opts.learningRate = 0.001 ; +opts.weightDecay = 0.0005 ; +opts.momentum = 0.9 ; +opts.randomSeed = 0 ; +opts.stegoShuffle = false; +opts.computeBNMoment = false; +opts.memoryMapFile = fullfile(tempdir, 'matconvnet.bin') ; +opts.profile = false ; +opts.cudnn = true ; + +opts.derOutputs = {'objective', 1} ; +opts.extractStatsFn = @extractStats ; +opts.plotStatistics = true; +opts = vl_argparse(opts, varargin) ; + +if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end +if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end +if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end +if isnan(opts.train), opts.train = [] ; end + +% ------------------------------------------------------------------------- +% Initialization +% ------------------------------------------------------------------------- + +evaluateMode = isempty(opts.train) ; +if ~evaluateMode + if isempty(opts.derOutputs) + error('DEROUTPUTS must be specified when training.\n') ; + end +end + +state.getBatch = getBatch ; +stats = [] ; + +% ------------------------------------------------------------------------- +% Train and validate +% ------------------------------------------------------------------------- + +modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep)); +modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ; + +start = opts.continue * findLastCheckpoint(opts.expDir) ; +if start >= 1 + fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ; + [net, stats] = loadState(modelPath(start)) ; +end + +if ( opts.computeBNMoment == true ) + % Validation without using the moving average of BN momemnts + for i = 1:numel(net.layers) + if ( isa( net.layers(i).block, 'dagnn.BatchNorm') ) + net.layers(i).block.computeMoment = true; + end + end +end + + +for epoch=start+1:opts.numEpochs + + % Set the random seed based on the epoch and opts.randomSeed. + % This is important for reproducibility, including when training + % is restarted from a checkpoint. + + rng(epoch + opts.randomSeed) ; + prepareGPUs(opts, epoch == start+1) ; + + % Train for one epoch. + state.epoch = epoch ; + state.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ; + + %state.train = opts.train(randperm(numel(opts.train))) ; % shuffle + %state.val = opts.val(randperm(numel(opts.val))) ; + + % shuffle + if( opts.stegoShuffle == 1 ) + + N = numel(opts.train); + M = numel(opts.val); + Lab = max(1, numel(opts.gpus)); + + % M and N must be even, and multiple Lab + assert( ( rem(N, 2*Lab) == 0 ) & ( rem(M, 2*Lab) == 0 ) ); + +% state.train(1:2:N) = opts.train(2*randperm(N/2) - 1); +% state.train(2:2:N) = state.train(1:2:N) + 1; +% +% state.val(1:2:M) = opts.val(2*randperm(M/2) - 1); +% state.val(2:2:M) = state.val(1:2:M) + 1; + + seq = opts.train(2*randperm(N/2) - 1); + seq = reshape(seq, Lab, N/(2*Lab)); + state.train = reshape([seq; seq+1], 1, N); + + seq = opts.val(2*randperm(M/2) - 1); + seq = reshape(seq, Lab, M/(2*Lab)); + state.val = reshape([seq; seq+1], 1, M); + + elseif ( opts.stegoShuffle < 0 ) + % for regression task + K = abs( opts.stegoShuffle ); + + M = numel(opts.train)/K; + seq = K * ( randperm(M) - 1 ); + seq = [seq + 1; seq + 2; seq + 3; seq + 4; seq + 5; seq + 6]; + seq = reshape(seq, numel(seq), 1); + state.train = opts.train(seq); + + + N = numel(opts.val)/K; + seq = K * ( randperm(N) - 1 ) ; + seq = [seq + 1; seq + 2; seq + 3; seq + 4; seq + 5; seq + 6]; + seq = reshape(seq, numel(seq), 1); + state.val = opts.val(seq); + + else + state.train = opts.train(randperm(numel(opts.train))) ; + state.val = opts.val(randperm(numel(opts.val))) ; + end + state.imdb = imdb ; + + if numel(opts.gpus) <= 1 + [stats.train(epoch),prof] = process_epoch(net, state, opts, 'train') ; + stats.val(epoch) = process_epoch(net, state, opts, 'val') ; + if opts.profile + profview(0,prof) ; + keyboard ; + end + else + savedNet = net.saveobj() ; + spmd + net_ = dagnn.DagNN.loadobj(savedNet) ; + [stats_.train, prof_] = process_epoch(net_, state, opts, 'train') ; + stats_.val = process_epoch(net_, state, opts, 'val') ; + if labindex == 1, savedNet_ = net_.saveobj() ; end + end + net = dagnn.DagNN.loadobj(savedNet_{1}) ; + stats__ = accumulateStats(stats_) ; + stats.train(epoch) = stats__.train ; + stats.val(epoch) = stats__.val ; + if opts.profile + mpiprofile('viewer', [prof_{:,1}]) ; + keyboard ; + end + clear net_ stats_ stats__ savedNet savedNet_ ; + end + + % save + if ~evaluateMode + saveState(modelPath(epoch), net, stats) ; + end + + if opts.plotStatistics + switchFigure(1) ; clf ; + plots = setdiff(... + cat(2,... + fieldnames(stats.train)', ... + fieldnames(stats.val)'), {'num', 'time'}) ; + for p = plots + p = char(p) ; + values = zeros(0, epoch) ; + leg = {} ; + for f = {'train', 'val'} + f = char(f) ; + if isfield(stats.(f), p) + tmp = [stats.(f).(p)] ; + values(end+1,:) = tmp(1,:)' ; + leg{end+1} = f ; + end + end + subplot(1,numel(plots),find(strcmp(p,plots))) ; + plot(1:epoch, values','o-') ; + xlabel('epoch') ; + title(p) ; + legend(leg{:}) ; + grid on ; + end + drawnow ; + print(1, modelFigPath, '-dpdf') ; + end +end + +if ( opts.computeBNMoment == true ) + % Revert it back + for i = 1:numel(net.layers) + if ( isa( net.layers(i).block, 'dagnn.BatchNorm') ) + net.layers(i).block.computeMoment = false; + end + end +end + +% ------------------------------------------------------------------------- +function [stats, prof] = process_epoch(net, state, opts, mode) +% ------------------------------------------------------------------------- + +% initialize empty momentum +if strcmp(mode,'train') + state.momentum = num2cell(zeros(1, numel(net.params))) ; +end + +% move CNN to GPU as needed +numGpus = numel(opts.gpus) ; +if numGpus >= 1 + net.move('gpu') ; + if strcmp(mode,'train') + state.momentum = cellfun(@gpuArray,state.momentum,'UniformOutput',false) ; + end +end +if numGpus > 1 + mmap = map_gradients(opts.memoryMapFile, net, numGpus) ; +else + mmap = [] ; +end + +% profile +if opts.profile + if numGpus <= 1 + profile clear ; + profile on ; + else + mpiprofile reset ; + mpiprofile on ; + end +end + +subset = state.(mode) ; +num = 0 ; +stats.num = 0 ; % return something even if subset = [] +stats.time = 0 ; +adjustTime = 0 ; + +start = tic ; +for t=1:opts.batchSize:numel(subset) + fprintf('%s: epoch %02d: %3d/%3d:', mode, state.epoch, ... + fix((t-1)/opts.batchSize)+1, ceil(numel(subset)/opts.batchSize)) ; + batchSize = min(opts.batchSize, numel(subset) - t + 1) ; + + for s=1:opts.numSubBatches + % get this image batch and prefetch the next + batchStart = t + (labindex-1) + (s-1) * numlabs ; + batchEnd = min(t+opts.batchSize-1, numel(subset)) ; + batch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ; + num = num + numel(batch) ; + if numel(batch) == 0, continue ; end + + inputs = state.getBatch(state.imdb, batch) ; + + if opts.prefetch + if s == opts.numSubBatches + batchStart = t + (labindex-1) + opts.batchSize ; + batchEnd = min(t+2*opts.batchSize-1, numel(subset)) ; + else + batchStart = batchStart + numlabs ; + end + nextBatch = subset(batchStart : opts.numSubBatches * numlabs : batchEnd) ; + state.getBatch(state.imdb, nextBatch) ; + end + + if strcmp(mode, 'train') + net.mode = 'normal' ; + net.accumulateParamDers = (s ~= 1) ; + net.eval(inputs, opts.derOutputs) ; + else + net.mode = 'test' ; + net.eval(inputs) ; + end + end + + % accumulate gradient + if strcmp(mode, 'train') + if ~isempty(mmap) + write_gradients(mmap, net) ; + labBarrier() ; + end + state = accumulate_gradients(state, net, opts, batchSize, mmap) ; + end + + % get statistics + time = toc(start) + adjustTime ; + batchTime = time - stats.time ; + stats = opts.extractStatsFn(net) ; + stats.num = num ; + stats.time = time ; + currentSpeed = batchSize / batchTime ; + averageSpeed = (t + batchSize - 1) / time ; + if t == opts.batchSize + 1 + % compensate for the first iteration, which is an outlier + adjustTime = 2*batchTime - time ; + stats.time = time + adjustTime ; + end + + fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ; + for f = setdiff(fieldnames(stats)', {'num', 'time'}) + f = char(f) ; + fprintf(' %s:', f) ; + fprintf(' %.3f', stats.(f)) ; + end + fprintf('\n') ; +end + +if ~isempty(mmap) + unmap_gradients(mmap) ; +end + +if opts.profile + if numGpus <= 1 + prof = profile('info') ; + profile off ; + else + prof = mpiprofile('info'); + mpiprofile off ; + end +else + prof = [] ; +end + +net.reset() ; +net.move('cpu') ; + +% ------------------------------------------------------------------------- +function state = accumulate_gradients(state, net, opts, batchSize, mmap) +% ------------------------------------------------------------------------- +numGpus = numel(opts.gpus) ; +otherGpus = setdiff(1:numGpus, labindex) ; + +for p=1:numel(net.params) + + % accumualte gradients from multiple labs (GPUs) if needed + if numGpus > 1 + tag = net.params(p).name ; + for g = otherGpus + tmp = gpuArray(mmap.Data(g).(tag)) ; + net.params(p).der = net.params(p).der + tmp ; + end + end + + switch net.params(p).trainMethod + + case 'average' % mainly for batch normalization + thisLR = net.params(p).learningRate ; + net.params(p).value = ... + (1 - thisLR) * net.params(p).value + ... + (thisLR/batchSize/net.params(p).fanout) * net.params(p).der ; + + case 'gradient' + thisDecay = opts.weightDecay * net.params(p).weightDecay ; + thisLR = state.learningRate * net.params(p).learningRate ; + state.momentum{p} = opts.momentum * state.momentum{p} ... + - thisDecay * net.params(p).value ... + - (1 / batchSize) * net.params(p).der ; + net.params(p).value = net.params(p).value + thisLR * state.momentum{p} ; + + case 'otherwise' + error('Unknown training method ''%s'' for parameter ''%s''.', ... + net.params(p).trainMethod, ... + net.params(p).name) ; + end +end + +% ------------------------------------------------------------------------- +function mmap = map_gradients(fname, net, numGpus) +% ------------------------------------------------------------------------- +format = {} ; +for i=1:numel(net.params) + format(end+1,1:3) = {'single', size(net.params(i).value), net.params(i).name} ; +end +format(end+1,1:3) = {'double', [3 1], 'errors'} ; +if ~exist(fname) && (labindex == 1) + f = fopen(fname,'wb') ; + for g=1:numGpus + for i=1:size(format,1) + fwrite(f,zeros(format{i,2},format{i,1}),format{i,1}) ; + end + end + fclose(f) ; +end +labBarrier() ; +mmap = memmapfile(fname, ... + 'Format', format, ... + 'Repeat', numGpus, ... + 'Writable', true) ; + +% ------------------------------------------------------------------------- +function write_gradients(mmap, net) +% ------------------------------------------------------------------------- +for i=1:numel(net.params) + mmap.Data(labindex).(net.params(i).name) = gather(net.params(i).der) ; +end + +% ------------------------------------------------------------------------- +function unmap_gradients(mmap) +% ------------------------------------------------------------------------- + +% ------------------------------------------------------------------------- +function stats = accumulateStats(stats_) +% ------------------------------------------------------------------------- + +for s = {'train', 'val'} + s = char(s) ; + total = 0 ; + + % initialize stats stucture with same fields and same order as + % stats_{1} + stats__ = stats_{1} ; + names = fieldnames(stats__.(s))' ; + values = zeros(1, numel(names)) ; + fields = cat(1, names, num2cell(values)) ; + stats.(s) = struct(fields{:}) ; + + for g = 1:numel(stats_) + stats__ = stats_{g} ; + num__ = stats__.(s).num ; + total = total + num__ ; + + for f = setdiff(fieldnames(stats__.(s))', 'num') + f = char(f) ; + stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ; + + if g == numel(stats_) + stats.(s).(f) = stats.(s).(f) / total ; + end + end + end + stats.(s).num = total ; +end + +% ------------------------------------------------------------------------- +function stats = extractStats(net) +% ------------------------------------------------------------------------- +sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ; +stats = struct() ; +for i = 1:numel(sel) + stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ; +end + +% ------------------------------------------------------------------------- +function saveState(fileName, net, stats) +% ------------------------------------------------------------------------- +net_ = net ; +net = net_.saveobj() ; +save(fileName, 'net', 'stats') ; + +% ------------------------------------------------------------------------- +function [net, stats] = loadState(fileName) +% ------------------------------------------------------------------------- +load(fileName, 'net', 'stats') ; +net = dagnn.DagNN.loadobj(net) ; + +% ------------------------------------------------------------------------- +function epoch = findLastCheckpoint(modelDir) +% ------------------------------------------------------------------------- +list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ; +tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ; +epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ; +epoch = max([epoch 0]) ; + +% ------------------------------------------------------------------------- +function switchFigure(n) +% ------------------------------------------------------------------------- +if get(0,'CurrentFigure') ~= n + try + set(0,'CurrentFigure',n) ; + catch + figure(n) ; + end +end + +% ------------------------------------------------------------------------- +function prepareGPUs(opts, cold) +% ------------------------------------------------------------------------- +numGpus = numel(opts.gpus) ; +if numGpus > 1 + % check parallel pool integrity as it could have timed out + pool = gcp('nocreate') ; + if ~isempty(pool) && pool.NumWorkers ~= numGpus + delete(pool) ; + end + pool = gcp('nocreate') ; + if isempty(pool) + parpool('local', numGpus) ; + cold = true ; + end + if exist(opts.memoryMapFile) + delete(opts.memoryMapFile) ; + end + +end +if numGpus >= 1 && cold + fprintf('%s: resetting GPU\n', mfilename) + if numGpus == 1 + gpuDevice(opts.gpus) + else + spmd, gpuDevice(opts.gpus(labindex)), end + end +end + +%end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/Abs.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/Abs.m new file mode 100644 index 0000000..5899c8c --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/Abs.m @@ -0,0 +1,12 @@ +classdef Abs < dagnn.ElementWise + methods + function outputs = forward(obj, inputs, params) + outputs{1} = vl_nnabs(inputs{1}) ; + end + + function [derInputs, derParams] = backward(obj, inputs, params, derOutputs) + derInputs{1} = vl_nnabs(inputs{1}, derOutputs{1}) ; + derParams = {} ; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/BatchNorm.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/BatchNorm.m new file mode 100644 index 0000000..8f59d25 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/BatchNorm.m @@ -0,0 +1,60 @@ +classdef BatchNorm < dagnn.ElementWise + properties + numChannels + epsilon = 1e-4 + computeMoment = false; + end + + properties (Transient) + moments + end + + methods + function outputs = forward(obj, inputs, params) + if strcmp(obj.net.mode, 'test') + if( obj.computeMoment ) + [outputs{1}, obj.moments] = vl_nnbnorm(inputs{1}, params{1}, ... + params{2}, 'epsilon', obj.epsilon) ; + else + outputs{1} = vl_nnbnorm(inputs{1}, params{1}, params{2}, ... + 'moments', params{3}, ... + 'epsilon', obj.epsilon) ; + obj.moments = []; + end + + else + [outputs{1}, obj.moments] = vl_nnbnorm(inputs{1}, params{1}, ... + params{2}, 'epsilon', obj.epsilon) ; + end + end + + function [derInputs, derParams] = backward(obj, inputs, params, derOutputs) + [derInputs{1}, derParams{1}, derParams{2}, derParams{3}] = ... + vl_nnbnorm(inputs{1}, params{1}, params{2}, derOutputs{1}, ... + 'epsilon', obj.epsilon) ; + obj.moments = []; + % multiply the moments update by the number of images in the batch + % this is required to make the update additive for subbatches + % and will eventually be normalized away + derParams{3} = derParams{3} * size(inputs{1},4) ; + end + + % --------------------------------------------------------------------- + function obj = BatchNorm(varargin) + obj.load(varargin{:}) ; + end + + function params = initParams(obj) + params{1} = ones(obj.numChannels,1,'single') ; + params{2} = zeros(obj.numChannels,1,'single') ; + params{3} = zeros(obj.numChannels,2,'single') ; + end + + function attach(obj, net, index) + attach@dagnn.ElementWise(obj, net, index) ; + p = net.getParamIndex(net.layers(index).params{3}) ; + net.params(p).trainMethod = 'average' ; + net.params(p).learningRate = 0.01 ; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/PhaseSplit.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/PhaseSplit.m new file mode 100644 index 0000000..69fac13 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/PhaseSplit.m @@ -0,0 +1,36 @@ +classdef PhaseSplit < dagnn.Filter + % Construct PhaseSplit in a way similar to Pooling, mightbe we could + % do it in a better and clean way. + properties + poolSize = [1 1] + end + + methods + function outputs = forward(self, inputs, params) + outputs{1} = vl_nnphasesplit( inputs{1} ) ; + end + + function [derInputs, derParams] = backward(self, inputs, params, derOutputs) + derInputs{1} = vl_nnphasesplit( inputs{1}, derOutputs{1} ) ; + derParams = {} ; + end + + function kernelSize = getKernelSize(obj) + kernelSize = obj.poolSize ; + end + + function outputSizes = getOutputSizes(obj, inputSizes) + %outputSizes = getOutputSizes@dagnn.Filter(obj, inputSizes) ; + outputSizes{1}(1) = inputSizes{1}(1)/8 ; + outputSizes{1}(2) = inputSizes{1}(2)/8; + outputSizes{1}(3) = inputSizes{1}(3)*64; + outputSizes{1}(4) = inputSizes{1}(4); + end + + function obj = PhaseSplit(varargin) + %obj.load(varargin) ; + obj.pad = [0 0 0 0]; + obj.stride = [8 8]; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/TanH.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/TanH.m new file mode 100644 index 0000000..d4e6a8e --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/TanH.m @@ -0,0 +1,12 @@ +classdef TanH < dagnn.ElementWise + methods + function outputs = forward(obj, inputs, params) + outputs{1} = vl_nntanh(inputs{1}) ; + end + + function [derInputs, derParams] = backward(obj, inputs, params, derOutputs) + derInputs{1} = vl_nntanh(inputs{1}, derOutputs{1}) ; + derParams = {} ; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/fromSimpleNN.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/fromSimpleNN.m new file mode 100644 index 0000000..abd26cc --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/+dagnn/fromSimpleNN.m @@ -0,0 +1,236 @@ +function obj = fromSimpleNN(net, varargin) +% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network +% FROMSIMPLENN(NET) initializes the DagNN object from the +% specified CNN using the SimpleNN format. +% +% SimpleNN objects are linear chains of computational layers. These +% layers exchange information through variables and parameters that +% are not explicitly named. Hence, FROMSIMPLENN() uses a number of +% rules to assign such names automatically: +% +% * From the input to the output of the CNN, variables are called +% `x0` (input of the first layer), `x1`, `x2`, .... In this +% manner `xi` is the output of the i-th layer. +% +% * Any loss layer requires two inputs, the second being a label. +% These are called `label` (for the first such layers), and then +% `label2`, `label3`,... for any other similar layer. +% +% Additionally, given the option `CanonicalNames` the function can +% change the names of some variables to make them more convenient to +% use. With this option turned on: +% +% * The network input is called `input` instead of `x0`. +% +% * The output of each SoftMax layer is called `prob` (or `prob2`, +% ...). +% +% * The output of each Loss layer is called `objective` (or ` +% objective2`, ...). +% +% * The input of each SoftMax or Loss layer of type *softmax log +% loss* is called `prediction` (or `prediction2`, ...). If a Loss +% layer immediately follows a SoftMax layer, then the rule above +% takes precendence and the input name is not changed. +% +% FROMSIMPLENN(___, 'OPT', VAL, ...) accepts the following options: +% +% `CanonicalNames`:: false +% If `true` use the rules above to assign more meaningful +% names to some of the variables. + +% Copyright (C) 2015 Karel Lenc and Andrea Vedaldi. +% All rights reserved. +% +% This file is part of the VLFeat library and is made available under +% the terms of the BSD license (see the COPYING file). + +opts.canonicalNames = false ; +opts = vl_argparse(opts, varargin) ; + +import dagnn.* + +obj = DagNN() ; +net = vl_simplenn_move(net, 'cpu') ; +net = vl_simplenn_tidy(net) ; + +% copy meta-information as is +obj.meta = net.meta ; + +for l = 1:numel(net.layers) + inputs = {sprintf('x%d',l-1)} ; + outputs = {sprintf('x%d',l)} ; + + params = struct(... + 'name', {}, ... + 'value', {}, ... + 'learningRate', [], ... + 'weightDecay', []) ; + if isfield(net.layers{l}, 'name') + name = net.layers{l}.name ; + else + name = sprintf('layer%d',l) ; + end + + switch net.layers{l}.type + case {'conv', 'convt'} + sz = size(net.layers{l}.weights{1}) ; + hasBias = ~isempty(net.layers{l}.weights{2}) ; + params(1).name = sprintf('%sf',name) ; + params(1).value = net.layers{l}.weights{1} ; + if hasBias + params(2).name = sprintf('%sb',name) ; + params(2).value = net.layers{l}.weights{2} ; + end + if isfield(net.layers{l},'learningRate') + params(1).learningRate = net.layers{l}.learningRate(1) ; + if hasBias + params(2).learningRate = net.layers{l}.learningRate(2) ; + end + end + if isfield(net.layers{l},'weightDecay') + params(1).weightDecay = net.layers{l}.weightDecay(1) ; + if hasBias + params(2).weightDecay = net.layers{l}.weightDecay(2) ; + end + end + switch net.layers{l}.type + case 'conv' + block = Conv() ; + block.size = sz ; + block.pad = net.layers{l}.pad ; + block.stride = net.layers{l}.stride ; + case 'convt' + block = ConvTranspose() ; + block.size = sz ; + block.upsample = net.layers{l}.upsample ; + block.crop = net.layers{l}.crop ; + block.numGroups = net.layers{l}.numGroups ; + end + block.hasBias = hasBias ; + block.opts = net.layers{l}.opts ; + + case 'pool' + block = Pooling() ; + block.method = net.layers{l}.method ; + block.poolSize = net.layers{l}.pool ; + block.pad = net.layers{l}.pad ; + block.stride = net.layers{l}.stride ; + block.opts = net.layers{l}.opts ; + + case 'phasesplit' + block = PhaseSplit(); + + case {'normalize', 'lrn'} + block = LRN() ; + block.param = net.layers{l}.param ; + + case {'dropout'} + block = DropOut() ; + block.rate = net.layers{l}.rate ; + + case {'relu'} + block = ReLU() ; + block.leak = net.layers{l}.leak ; + + case {'sigmoid'} + block = Sigmoid() ; + + case {'abs'} + block = Abs(); + + case {'tanh'} + block = TanH(); + + case {'tlu'} + block = TLU(); + + case {'softmax'} + block = SoftMax() ; + + case {'softmaxloss'} + block = Loss('loss', 'softmaxlog') ; + % The loss has two inputs + inputs{2} = getNewVarName(obj, 'label') ; + + case {'l2'} + block = Loss('loss', 'l2') ; + % The loss has two inputs + inputs{2} = getNewVarName(obj, 'label') ; + + case {'bnorm'} + block = BatchNorm() ; + params(1).name = sprintf('%sm',name) ; + params(1).value = net.layers{l}.weights{1} ; + params(2).name = sprintf('%sb',name) ; + params(2).value = net.layers{l}.weights{2} ; + params(3).name = sprintf('%sx',name) ; + params(3).value = net.layers{l}.weights{3} ; + if isfield(net.layers{l},'learningRate') + params(1).learningRate = net.layers{l}.learningRate(1) ; + params(2).learningRate = net.layers{l}.learningRate(2) ; + params(3).learningRate = net.layers{l}.learningRate(3) ; + end + if isfield(net.layers{l},'weightDecay') + params(1).weightDecay = net.layers{l}.weightDecay(1) ; + params(2).weightDecay = net.layers{l}.weightDecay(2) ; + params(3).weightDecay = 0 ; + end + + otherwise + error([net.layers{l}.type ' is unsupported']) ; + end + + obj.addLayer(... + name, ... + block, ... + inputs, ... + outputs, ... + {params.name}) ; + + for p = 1:numel(params) + pindex = obj.getParamIndex(params(p).name) ; + if ~isempty(params(p).value) + obj.params(pindex).value = params(p).value ; + end + if ~isempty(params(p).learningRate) + obj.params(pindex).learningRate = params(p).learningRate ; + end + if ~isempty(params(p).weightDecay) + obj.params(pindex).weightDecay = params(p).weightDecay ; + end + end +end + +% -------------------------------------------------------------------- +% Rename variables to canonical names +% -------------------------------------------------------------------- + +if opts.canonicalNames + for l = 1:numel(obj.layers) + if l == 1 + obj.renameVar(obj.layers(l).inputs{1}, 'input') ; + end + if isa(obj.layers(l).block, 'dagnn.SoftMax') + obj.renameVar(obj.layers(l).outputs{1}, getNewVarName(obj, 'prob')) ; + obj.renameVar(obj.layers(l).inputs{1}, getNewVarName(obj, 'prediction')) ; + end + if isa(obj.layers(l).block, 'dagnn.Loss') %|| isa(obj.layers(l).block, 'dagnn.L2Dist') + obj.renameVar(obj.layers(l).outputs{1}, 'objective') ; + if isempty(regexp(obj.layers(l).inputs{1}, '^prob.*')) + obj.renameVar(obj.layers(l).inputs{1}, ... + getNewVarName(obj, 'prediction')) ; + end + end + end +end + +% -------------------------------------------------------------------- +function name = getNewVarName(obj, prefix) +% -------------------------------------------------------------------- +t = 0 ; +name = prefix ; +while any(strcmp(name, {obj.vars.name})) + t = t + 1 ; + name = sprintf('%s%d', prefix, t) ; +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nnabs.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nnabs.m new file mode 100644 index 0000000..10a63fe --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nnabs.m @@ -0,0 +1,19 @@ +function out = vl_nnabs(x,dzdy) +%VL_NNABS CNN ABS unit. +% Y = VL_NNABS(X) computes the absolute value of the data X. X can +% have an arbitrary size. The abs is defined as follows: +% +% ABS(X) = |X|. +% +% DZDX = VL_NNABS(X, DZDY) computes the derivative of the +% block projected onto DZDY. DZDX and DZDY have the same +% dimensions as X and Y respectively. + +% Note, MATLAB built-in function ABS() and SIGN() are used because their +% support for gpuArray + +if nargin <= 1 || isempty(dzdy) + out = abs( x ) ; +else + out = dzdy .* sign( x ); +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nnphasesplit.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nnphasesplit.m new file mode 100644 index 0000000..e92fb00 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nnphasesplit.m @@ -0,0 +1,58 @@ +function y = vl_nnphasesplit( x, dzdy ) +%VL_NNPHASESPLIT CNN phase split the feature plane into 8x8 = 64 DCT mode. +% Y = VL_NNCROP(X, STRIDE) phase split the input X into 64 DCT phase mode. +% +% DZDX = VL_NNCROP(X, DZDY) computes the derivative DZDX of the +% function projected on the output derivative DZDY. DZDX has the same +% dimension as X and DZDY the same dimension as Y. +% + +% dimension must be divided by 8 +assert( rem(size(x,1), 8 ) == 0 & rem( size(x,2), 8) == 0 ); + +% Initialize some parameters +inputSize = [size(x,1) size(x,2) size(x,3) size(x,4)] ; +outputSize = [size(x,1)/8, size(x,2)/8, 64*size(x,3), size(x,4)]; + +% zig zag order +zzag = zeros(64, 4); +idx = 1; +startCh = 1; +for i = 0:7 + for j = 0:7 + stopCh = startCh + inputSize(3); + zzag(idx, :) = [ i, j, startCh, stopCh - 1 ]; + idx = idx + 1; + startCh = stopCh; + end +end + +% sampling array +sy = 1:8:inputSize(1); +sx = 1:8:inputSize(2); + +if nargin <= 1 || isempty(dzdy) + % forward function + if isa( x, 'gpuArray' ) + y = gpuArray.zeros(outputSize, classUnderlying(x)) ; + else + y = zeros(outputSize, 'like', x ) ; + end + + for i = 1:64 + y(:,:,zzag(i,3):zzag(i,4),:) = x(sy + zzag(i,1), sx + zzag(i,2), :, : ); + end + +else + % backward function + if isa(dzdy, 'gpuArray') + y = gpuArray.zeros(inputSize, classUnderlying(dzdy)) ; + else + y = zeros(inputSize, 'like', x) ; + end + + for i = 1:64 + y(sy + zzag(i,1), sx + zzag(i,2), :, : ) = dzdy(:,:,zzag(i,3):zzag(i,4),:); + end + +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nntanh.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nntanh.m new file mode 100644 index 0000000..11ce572 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/vl_nntanh.m @@ -0,0 +1,21 @@ +function out = vl_nntanh(x,dzdy) +%VL_NNTANH CNN TanH hyperbolic non-linearity +% Y = VL_NNTANH(X) computes the hyperbolic tangent non-linearity of the +% data X. X can have an arbitrary size. The tanh is defined as follows: +% +% TANH(X) = (EXP(2X) - 1 )/( EXP(2x) + 1 ). +% +% DZDX = VL_NNTANH(X, DZDY) computes the derivative of the +% block projected onto DZDY. DZDX and DZDY have the same +% dimensions as X and Y respectively. +% +% NOTE: Matlab build-in function TANH() is used since it has extended +% support for gpuArray + +y = tanh( x ); + +if nargin <= 1 || isempty(dzdy) + out = y; +else + out = dzdy .* ( 1 - y.*y ); +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nnabs.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nnabs.m new file mode 100644 index 0000000..d80ffa9 --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nnabs.m @@ -0,0 +1,36 @@ +classdef nnabs < nntest + properties + x + delta + end + + methods (TestClassSetup) + function data(test,device) + % make sure that all elements in x are differentiable. in this way, + % we can compute numerical derivatives reliably by adding a delta < .5. + delta = 0.01; + test.range = 10 ; + x = test.randn(15,14,3,2) ; + + ind = find(( x < 0 )&( x > -2*delta)); + if (~isempty(ind)) + x(ind) = -2 + rand([1, length(ind)], 'like', x); + end + + test.x = x ; + test.delta = delta; + + if strcmp(device,'gpu'), test.x = gpuArray(test.x) ; end + end + end + + methods (Test) + function basic(test) + x = test.x ; + y = vl_nnabs(x) ; + dzdy = test.randn(size(y)) ; + dzdx = vl_nnabs(x,dzdy) ; + test.der(@(x) vl_nnabs(x), x, dzdy, dzdx, 1e-2) ; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nnphasesplit.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nnphasesplit.m new file mode 100644 index 0000000..056c14d --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nnphasesplit.m @@ -0,0 +1,23 @@ +classdef nnphasesplit < nntest + properties + x + end + + methods (TestClassSetup) + function data(test,device) + test.range = 10 ; + test.x = test.randn(32,32,4,2) ; + if strcmp(device,'gpu'), test.x = gpuArray(test.x) ; end + end + end + + methods (Test) + function basic(test) + x = test.x ; + y = vl_nnphasesplit(x) ; + dzdy = test.randn(size(y)) ; + dzdx = vl_nnphasesplit(x,dzdy) ; + test.der(@(x) vl_nnphasesplit(x), x, dzdy, dzdx, 1e-3) ; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nntanh.m b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nntanh.m new file mode 100644 index 0000000..020b16e --- /dev/null +++ b/PhaseAwareNet_SRC/MatConvNet/matconvnet-1.0-beta20/matlab/xtest/suite/nntanh.m @@ -0,0 +1,24 @@ +classdef nntanh < nntest + properties + x + delta + end + + methods (TestClassSetup) + function data(test,device) + test.range = 10 ; + test.x = test.randn(15,14,3,2) ; + if strcmp(device,'gpu'), test.x = gpuArray(test.x) ; end + end + end + + methods (Test) + function basic(test) + x = test.x ; + y = vl_nntanh(x) ; + dzdy = test.randn(size(y)) ; + dzdx = vl_nntanh(x,dzdy) ; + test.der(@(x) vl_nntanh(x), x, dzdy, dzdx, 1e-2) ; + end + end +end diff --git a/PhaseAwareNet_SRC/MatConvNet/readme.docx b/PhaseAwareNet_SRC/MatConvNet/readme.docx new file mode 100644 index 0000000..d7a8d70 Binary files /dev/null and b/PhaseAwareNet_SRC/MatConvNet/readme.docx differ diff --git a/README.md b/README.md index 9023c1b..58b84b0 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,134 @@ -# Steganalysis +# Steganography -Python 实现LSB算法进行信息隐藏 包含空域与变换域 JPEG信息隐藏算法 对PDF文件进行信息隐藏 基于卷积神经网络的隐写分析 Matlab SRM、SCA隐写分析 \ No newline at end of file +# 空域编码图像 + +* 空域编码是指在图像空间域进行编码,也就是直接针对图像像素进行编码 +* 对像素进行编码,如 LSB 算法,主要有下面两种方式 + * 光栅格式 + * 调色板格式 GIF(graphics interchange format) +* 一个图像编码标准往往包括多类编码方法,一个图像仅仅是其一类方法的实例。例如,常见的 BMP(Bitmap)、 TIFF( + Tagged Image File Format)、 PNG(Portable Network + Graphics)均支持光栅格式与调色板格式编码,对这两种格式 + 编码分别又支持多种具体编码方法 + +## LSB 隐写算法 + +* LSB 隐写是最基础、最简单的隐写方法,具有容量大、嵌入速度快、对载体图像质量影响小的特点 +* LSB 的大意就是最低比特位隐写。我们将深度为 8 的 BMP 图像,分为 8 个二值平面(位平面),我们将待嵌入的信息(info)直接写到最低的位平面上。换句话说,如果秘密信息与最低比特位相同,则不改动;如果秘密信息与最低比特位不同,则使用秘密信息值代替最低比特位 + +[代码实现](/LSB) + +![](https://www.writebug.com/myres/static/uploads/2021/12/30/d2a334c6057be058e3bdaa79efb202fb.writebug) +嵌入信息前的载体图片 + +![](https://www.writebug.com/myres/static/uploads/2021/12/30/5ef450e6b4aa705dfab9eaeffb47091f.writebug) +嵌入信息后的载体图片 + +# 变换域编码图像 + +## JPEG + +* Joint Photographic Experts Group(联合图像专家小组)的缩写 +* JPEG 编码 + ![](https://www.writebug.com/myres/static/uploads/2021/12/30/e251af7a91f2675226c55b1f2dc29186.writebug) + +### JSteg 隐写 + +* JSteg 的算法的主要思想是将秘密消息嵌入在量化后的 DCT 系数的最低比特位上,但对原始值为 0、+1、-1 的 DCT 系数不进行嵌入,提取秘密消息时,只需将载密图像中不等于 0、l 的量化 DCT 系数的 LSB 取出即可 +* JSteg 算法步骤 + +1. 选择载体图像,并且将载体图像划分为连续的 8×8 的子块。 +2. 对每个子块使用离散余弦变换之后,用相应的质量因数的量化表量化,得到对应的 8×8 量化 DCT 子块。 +3. 将需要隐藏的信息编码为二进制数据流,对 DCT 子块系数进行 Z 字形扫描,并且使用秘密信息的二进制流替换非 0 和非 1 的 DCT 系数的最低比特位。 +4. 进行熵编码等,产生 JPEG 隐密图像。 + +* JSteg 的具体嵌入过程 + +1. 部分解码 JPEG 图像,得到二进制存储的 AC 系数,判断该 AC 系数是否等于正负 1 或 0,若等于则跳过该 AC 系数,否则,执行下一步 +2. 判断二进制存储的 AC 系数的 LSB 是否与要嵌入的秘密信息比特相同,若相同,则不对其进行修改,否则执行下一步 +3. 用秘密信息比特替换二进制存储的 AC 系数的 LSB,将修改后的 AC 系数重新编码得到隐秘 JPEG 图像 + +* JSteg 不使用 0、1 的原因 + +1. DCT 系数中“0”的比例最大(一般可达到 60% 以上,取决于图像质量和压缩因子),压缩编码是利用大量出现连零实现的,如果改变 DCT 系数中“0”的话,不能很好的实现压缩 +2. DCT 系数中的“1”若变成“0”,由于接受端无法区分未使用的“0”和嵌入消息后得到的“0”,从而无法实现秘密信息的提取 + +[代码实现](Jsteg.py) + +### F3 隐写 + +* 为了改善大量 DCT 系数不隐藏信息这一状况,人们提出了 F3 隐写 +* F3 对原始值为 +1 和-1 的 DCT 系数,进行了利用。F3 隐写的规则如下 + +1. 每个非 0 的 DCT 数据用于隐藏 1 比特秘密信息,为 0 的 DCT 系数不负载秘密信息 +2. 如果秘密信息与 DCT 的 LSB 相同,便不作改动;如果不同,将 DCT 系数的绝对值减小 1,符号不变 +3. 当原始值为 +1 或-1 且预嵌入秘密信息为 0 时,将这个位置归 0 并视为无效,在下一个 DCT 系数上重新嵌入 + +* 编写代码实现嵌入,并观察 DCT 系数变化 + [代码实现](F3.py) + +``` +JPEG的DCT系数 +{0: 32939, 1: 15730, 2: 13427, 3: 11523, 4: 9540, 5: 7957, 6: 6607, 7: 5697, 8: 4834, -1: 15294, -2: 13637, -3: 11479, -4: 9683, -5: 7979, -6: 6878, -7: 5631, -8: 4871} +Jsteg begin writing! +>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +经过信息隐藏后JPEG的DCT系数变化 +{0: 32939, 1: 15730, 2: 12552, 3: 12398, 4: 8739, 5: 8758, 6: 6165, 7: 6139, 8: 4487, -1: 15294, -2: 12721, -3: 12395, -4: 8891, -5: 8771, -6: 6319, -7: 6190, -8: 4463} +F3steg begin writing! +>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +经过信息隐藏后JPEG的DCT系数变化 +{0: 47068, 1: 13416, 2: 13519, 3: 10075, 4: 9545, 5: 7077, 6: 6650, 7: 5016, 8: 4754, -1: 13308, -2: 13668, -3: 10124, -4: 9571, -5: 7249, -6: 6591, -7: 5098, -8: 4733} +F4steg begin writing! +>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +经过信息隐藏后JPEG的DCT系数变化 +{0: 59320, 1: 13618, 2: 11987, 3: 9875, 4: 8328, 5: 6860, 6: 5883, 7: 4910, 8: 4239, -1: 13692, -2: 11976, -3: 9976, -4: 8428, -5: 7007, -6: 5834, -7: 4964, -8: 4190} +``` + +* 条形图绘制 + +![](https://www.writebug.com/myres/static/uploads/2021/12/30/7a40002ebbd1e4d6e160bcce7b50f636.writebug) + +* 未经过信息隐藏的 DCT 系数,系数近似符合拉普拉斯分布,具有几个典型特点 + * 对称性 以 0 为中心达到最大值,两侧分布近似对称 + * 单侧单调性 以 0 值为中心达到最大值,两侧单调下降 + * 梯度下降性 小值样点较多,大值样点较少,分布曲线在两侧下降梯度逐渐减小 + ![](https://www.writebug.com/myres/static/uploads/2021/12/30/dd1e5a48b8e006d7456a3ba1f2c4ef35.writebug) +* JSteg 隐写的 DCT 系数 + * JSteg 隐写可嵌入信息的 DCT 系数较少,隐写量较小,且相邻数值样点的个数接近,如 2 和 3,-2 和-3 形成了值对,卡方特征变化明显,因而提出了 F3 隐写 + ![](https://www.writebug.com/myres/static/uploads/2021/12/30/5231d6a6b7690459b0c318d62221b60e.writebug) +* F3 隐写的 DCT 系数 + * F3 的设计虽然防止了相邻值出现数量接近的现象,也维持了分布函数的对称性,但使得偶数的分布增加,没有满足单调性 + * 这是因为载体绝对值为 1 的数值较多,当其被修改为 0 时,嵌入算法继续嵌入直到找到一个偶数值,或者将一个奇数值改为偶数值,这样绝对值为 1 的系数可以支持嵌入 1,但是不支持嵌入 0,需要使用或制造一个偶数 + * 另外,0 系数的数量有相应的增加,产生分布曲线向 0 收缩的现象 + +### F4 隐写 + +* 为了克服 F3 的缺陷,F4 对不同正负号的奇偶系数采用了不同的嵌入与消息表示方法 +* **F4 用负偶数、正奇数代表嵌入了消息比特 1,用负奇数、正偶数代表嵌入了 0** +* 但仍然通过减小绝对值的方法进行修改,如果减小绝对值后系数为 0 则继续往下嵌入当前比特 +* [代码实现](F4.py) + ![](https://www.writebug.com/myres/static/uploads/2021/12/30/cf7f25ee63efd8a6f3f3b0688ca49fe1.writebug) +* F4 隐写的 DCT 系数 + * F4 显然保持了载体分布函数的对称性,也保持了载体分布函数的单调性与梯度下降性 + * 但 F4 依然存在使含密载体分布函数形状向 0 收缩的现象 + +### F5 隐写 + +F5 隐写实现了基于汉明码的矩阵编码隐写,在一个分组上最多修改 R=1 次以嵌入 $2^r-1-r$ 比特,采用的基本嵌入方法是基于 F4 隐写的 + +F5 的嵌入步骤 + +- 获得嵌入域。若输入的是位图,则进行 JPEG 编码得到 JPEG 系数;若输入的是 JPEG 图像,则进行熵编码的解码得到 JPEG 系数 +- 位置置乱。根据口令生成的密钥位一个伪随机数发生器,基于伪随机数发生器置乱 JPEG 系数的位置 +- 编码参数确定。为了提高嵌入效率,一般希望 n 尽可能大,因此,根据载体中可用系数的数量与消息的长度确定参数 r,并计算$n=2^r-1$ +- 基于($n=2^r-1,r$)的汉明分组码得到编码校验矩阵,开始嵌入消息: + - ① 按置乱后的顺序取下面 n 个非零系数,在其中的 LSB 序列中按照以上编码嵌入 n-r 比特的消息; + - ② 如果未发生修改,并且还有需要嵌入的消息,则返回 ① 继续嵌入下一分组; + - ③ 如果进行了修改,则判断是不是有系数值收缩到 0,如果没有,并且还有需要嵌入的消息则返回 ① 继续嵌入下一分组,如果有,取出一个新的非零系数组成新的一组 n 个非零系数,在其中的 LSB 序列中按照以上编码重新嵌入以上 n-r 比特的消息,直到没有修改或收缩,最后,如果还有需要嵌入的消息,则返回 ① 继续嵌入下一分组 +- 位置逆置乱。恢复 DCT 系数原来的位置顺序 +- 熵编码。按照 JPEG 标准无损压缩 DCT 量化系数,得到 JPEG 文件 + +# 参考资料 + +* 隐写学原理与技术 By 赵险峰 +* 数字媒体中的隐写术 By J.Fridrich diff --git a/SRNet/SCA_SRNet.py b/SRNet/SCA_SRNet.py new file mode 100644 index 0000000..70d00b1 --- /dev/null +++ b/SRNet/SCA_SRNet.py @@ -0,0 +1,118 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Sep 18 19:59:14 2019 + +@author: Lee +""" + +import os +import sys +os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' +os.environ['CUDA_VISIBLE_DEVICES'] = '1' # set a GPU (with GPU Number) +home = os.path.expanduser("~") +sys.path.append(home + '/tflib/') # path for 'tflib' folder +import matplotlib.pyplot as plt +from scipy.io import loadmat +from SCA_SRNet_Spatial import * # use 'SCA_SRNet_JPEG' for JPEG domain + + +def trnGen(cover_path, stego_path, cover_beta_path, stego_beta_path, thread_idx=0, n_threads=1): + IL=os.listdir(cover_path) + img_shape = plt.imread(cover_path +IL[0]).shape + batch = np.empty((2, img_shape[0], img_shape[1], 2), dtype='float32') + while True: + indx = np.random.permutation(len(IL)) + for i in indx: + batch[0,:,:,0] = plt.imread(cover_path + IL[i]) # use loadmat for loading JPEG decompressed images + batch[0,:,:,1] = loadmat(cover_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images + batch[1,:,:,0] = plt.imread(stego_path + IL[i]) # use loadmat for loading JPEG decompressed images + batch[1,:,:,1] = loadmat(stego_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images + rot = random.randint(0,3) + if rand() < 0.5: + yield [np.rot90(batch, rot, axes=[1,2]), np.array([0,1], dtype='uint8')] + else: + yield [np.flip(np.rot90(batch, rot, axes=[1,2]), axis=2), np.array([0,1], dtype='uint8')] + + +def valGen(cover_path, stego_path, cover_beta_path, stego_beta_path, thread_idx=0, n_threads=1): + IL=os.listdir(cover_path) + img_shape = plt.imread(cover_path +IL[0]).shape + batch = np.empty((2, img_shape[0], img_shape[1], 2), dtype='float32') + while True: + for i in range(len(IL)): + batch[0,:,:,0] = plt.imread(cover_path + IL[i]) # use loadmat for loading JPEG decompressed images + batch[0,:,:,1] = loadmat(cover_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images + batch[1,:,:,0] = plt.imread(stego_path + IL[i]) # use loadmat for loading JPEG decompressed images + batch[1,:,:,1] = loadmat(stego_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images + yield [batch, np.array([0,1], dtype='uint8') ] + + +train_batch_size = 32 +valid_batch_size = 40 +max_iter = 500000 +train_interval=100 +valid_interval=5000 +save_interval=5000 +num_runner_threads=10 + +# save Betas as '.mat' files with variable name "Beta" and put them in thier corresponding directoroies. Make sure +# all mat files in the directories can be loaded in Python without any errors. + +TRAIN_COVER_DIR = '/media/Cover_TRN/' +TRAIN_STEGO_DIR = '/media/Stego_WOW_0.5_TRN/' +TRAIN_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_TRN/' +TRAIN_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_TRN/' + +VALID_COVER_DIR = '/media/Cover_VAL/' +VALID_STEGO_DIR = '/media/Stego_WOW_0.5_VAL/' +VALID_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_VAL/' +VALID_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_VAL/' + +train_gen = partial(trnGen, \ + TRAIN_COVER_DIR, TRAIN_STEGO_DIR, TRAIN_COVER_BETA_DIR, TRAIN_STEGO_BETA_DIR) +valid_gen = partial(valGen, \ + VALID_COVER_DIR, VALID_STEGO_DIR, VALID_COVER_BETA_DIR, VALID_STEGO_BETA_DIR) + +LOG_DIR= '/media/LogFiles/SCA_WOW_0.5' # path for a log direcotry +# load_path= LOG_DIR + 'Model_460000.ckpt' # continue training from a specific checkpoint +load_path=None # training from scratch + +if not os.path.exists(LOG_DIR): + os.makedirs(LOG_DIR) + +train_ds_size = len(glob(TRAIN_COVER_DIR + '/*')) * 2 +valid_ds_size = len(glob(VALID_COVER_DIR +'/*')) * 2 +print 'train_ds_size: %i'%train_ds_size +print 'valid_ds_size: %i'%valid_ds_size + +if valid_ds_size % valid_batch_size != 0: + raise ValueError("change batch size for validation") + +optimizer = AdamaxOptimizer +boundaries = [400000] # learning rate adjustment at iteration 400K +values = [0.001, 0.0001] # learning rates +train(SCA_SRNet, train_gen, valid_gen , train_batch_size, valid_batch_size, valid_ds_size, \ + optimizer, boundaries, values, train_interval, valid_interval, max_iter,\ + save_interval, LOG_DIR,num_runner_threads, load_path) + + +# Testing +TEST_COVER_DIR = '/media/Cover_TST/' +TEST_STEGO_DIR = '/media/Stego_WOW_0.5_TST/' +TEST_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_TST/' +TEST_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_TST/' + +test_batch_size=40 +LOG_DIR = '/media/LogFiles/SCA_WOW_0.5/' +LOAD_DIR = LOG_DIR + 'Model_435000.ckpt' # loading from a specific checkpoint + +test_gen = partial(gen_valid, \ + TEST_COVER_DIR, TEST_STEGO_DIR) + +test_ds_size = len(glob(TEST_COVER_DIR + '/*')) * 2 +print 'test_ds_size: %i'%test_ds_size + +if test_ds_size % test_batch_size != 0: + raise ValueError("change batch size for testing!") + +test_dataset(SCA_SRNet, test_gen, test_batch_size, test_ds_size, LOAD_DIR) \ No newline at end of file diff --git a/SRNet/SCA_SRNet_Example.ipynb b/SRNet/SCA_SRNet_Example.ipynb new file mode 100644 index 0000000..c608db2 --- /dev/null +++ b/SRNet/SCA_SRNet_Example.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '1' # set a GPU (with GPU Number)\n", + "home = os.path.expanduser(\"~\")\n", + "sys.path.append(home + '/tflib/') # path for 'tflib' folder\n", + "import matplotlib.pyplot as plt\n", + "from scipy.io import loadmat\n", + "from SCA_SRNet_Spatial import * # use 'SCA_SRNet_JPEG' for JPEG domain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def trnGen(cover_path, stego_path, cover_beta_path, stego_beta_path, thread_idx=0, n_threads=1):\n", + " IL=os.listdir(cover_path)\n", + " img_shape = plt.imread(cover_path +IL[0]).shape\n", + " batch = np.empty((2, img_shape[0], img_shape[1], 2), dtype='float32')\n", + " while True:\n", + " indx = np.random.permutation(len(IL))\n", + " for i in indx:\n", + " batch[0,:,:,0] = plt.imread(cover_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", + " batch[0,:,:,1] = loadmat(cover_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", + " batch[1,:,:,0] = plt.imread(stego_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", + " batch[1,:,:,1] = loadmat(stego_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", + " rot = random.randint(0,3)\n", + " if rand() < 0.5:\n", + " yield [np.rot90(batch, rot, axes=[1,2]), np.array([0,1], dtype='uint8')]\n", + " else:\n", + " yield [np.flip(np.rot90(batch, rot, axes=[1,2]), axis=2), np.array([0,1], dtype='uint8')] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def valGen(cover_path, stego_path, cover_beta_path, stego_beta_path, thread_idx=0, n_threads=1):\n", + " IL=os.listdir(cover_path)\n", + " img_shape = plt.imread(cover_path +IL[0]).shape\n", + " batch = np.empty((2, img_shape[0], img_shape[1], 2), dtype='float32')\n", + " while True:\n", + " for i in range(len(IL)):\n", + " batch[0,:,:,0] = plt.imread(cover_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", + " batch[0,:,:,1] = loadmat(cover_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", + " batch[1,:,:,0] = plt.imread(stego_path + IL[i]) # use loadmat for loading JPEG decompressed images \n", + " batch[1,:,:,1] = loadmat(stego_beta_path + IL[i].replace('pgm','mat'))['Beta'] # adjust for JPEG images\n", + " yield [batch, np.array([0,1], dtype='uint8') ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_batch_size = 32\n", + "valid_batch_size = 40\n", + "max_iter = 500000\n", + "train_interval=100\n", + "valid_interval=5000\n", + "save_interval=5000\n", + "num_runner_threads=10\n", + "\n", + "# save Betas as '.mat' files with variable name \"Beta\" and put them in thier corresponding directoroies. Make sure \n", + "# all mat files in the directories can be loaded in Python without any errors.\n", + "\n", + "TRAIN_COVER_DIR = '/media/Cover_TRN/'\n", + "TRAIN_STEGO_DIR = '/media/Stego_WOW_0.5_TRN/'\n", + "TRAIN_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_TRN/'\n", + "TRAIN_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_TRN/'\n", + "\n", + "VALID_COVER_DIR = '/media/Cover_VAL/'\n", + "VALID_STEGO_DIR = '/media/Stego_WOW_0.5_VAL/'\n", + "VALID_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_VAL/'\n", + "VALID_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_VAL/'\n", + "\n", + "train_gen = partial(trnGen, \\\n", + " TRAIN_COVER_DIR, TRAIN_STEGO_DIR, TRAIN_COVER_BETA_DIR, TRAIN_STEGO_BETA_DIR) \n", + "valid_gen = partial(valGen, \\\n", + " VALID_COVER_DIR, VALID_STEGO_DIR, VALID_COVER_BETA_DIR, VALID_STEGO_BETA_DIR)\n", + "\n", + "LOG_DIR= '/media/LogFiles/SCA_WOW_0.5' # path for a log direcotry\n", + "# load_path= LOG_DIR + 'Model_460000.ckpt' # continue training from a specific checkpoint\n", + "load_path=None # training from scratch\n", + "\n", + "if not os.path.exists(LOG_DIR):\n", + " os.makedirs(LOG_DIR)\n", + " \n", + "train_ds_size = len(glob(TRAIN_COVER_DIR + '/*')) * 2\n", + "valid_ds_size = len(glob(VALID_COVER_DIR +'/*')) * 2\n", + "print 'train_ds_size: %i'%train_ds_size\n", + "print 'valid_ds_size: %i'%valid_ds_size\n", + "\n", + "if valid_ds_size % valid_batch_size != 0:\n", + " raise ValueError(\"change batch size for validation\")\n", + "\n", + "optimizer = AdamaxOptimizer\n", + "boundaries = [400000] # learning rate adjustment at iteration 400K\n", + "values = [0.001, 0.0001] # learning rates\n", + "train(SCA_SRNet, train_gen, valid_gen , train_batch_size, valid_batch_size, valid_ds_size, \\\n", + " optimizer, boundaries, values, train_interval, valid_interval, max_iter,\\\n", + " save_interval, LOG_DIR,num_runner_threads, load_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Testing \n", + "TEST_COVER_DIR = '/media/Cover_TST/'\n", + "TEST_STEGO_DIR = '/media/Stego_WOW_0.5_TST/'\n", + "TEST_COVER_BETA_DIR = '/media/Beta_Cover_WOW_0.5_TST/'\n", + "TEST_STEGO_BETA_DIR = '/media/Beta_Stego_WOW_0.5_TST/'\n", + "\n", + "test_batch_size=40\n", + "LOG_DIR = '/media/LogFiles/SCA_WOW_0.5/' \n", + "LOAD_DIR = LOG_DIR + 'Model_435000.ckpt' # loading from a specific checkpoint\n", + "\n", + "test_gen = partial(gen_valid, \\\n", + " TEST_COVER_DIR, TEST_STEGO_DIR)\n", + "\n", + "test_ds_size = len(glob(TEST_COVER_DIR + '/*')) * 2\n", + "print 'test_ds_size: %i'%test_ds_size\n", + "\n", + "if test_ds_size % test_batch_size != 0:\n", + " raise ValueError(\"change batch size for testing!\")\n", + "\n", + "test_dataset(SCA_SRNet, test_gen, test_batch_size, test_ds_size, LOAD_DIR)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/SRNet/SRNet_Example.ipynb b/SRNet/SRNet_Example.ipynb new file mode 100644 index 0000000..cf6be8e --- /dev/null +++ b/SRNet/SRNet_Example.ipynb @@ -0,0 +1,127 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '1' # set a GPU (with GPU Number)\n", + "home = os.path.expanduser(\"~\")\n", + "sys.path.append(home + '/tflib/') # path for 'tflib' folder\n", + "from SRNet import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_batch_size = 32\n", + "valid_batch_size = 40\n", + "max_iter = 500000\n", + "train_interval=100\n", + "valid_interval=5000\n", + "save_interval=5000\n", + "num_runner_threads=10\n", + "\n", + "# Cover and Stego directories for training and validation. For the spatial domain put cover and stego images in their \n", + "# corresponding direcotries. For the JPEG domain, decompress images to the spatial domain without rounding to integers and \n", + "# save them as '.mat' files with variable name \"im\". Put the '.mat' files in thier corresponding directoroies. Make sure \n", + "# all mat files in the directories can be loaded in Python without any errors.\n", + "\n", + "TRAIN_COVER_DIR = '/media/TRN/Cover/'\n", + "TRAIN_STEGO_DIR = '/media/TRN/JUNI_75_04/'\n", + "\n", + "VALID_COVER_DIR = '/media/VAL/Cover/'\n", + "VALID_STEGO_DIR = '/media/VAL/JUNI_75_04/'\n", + " \n", + "train_gen = partial(gen_flip_and_rot, \\\n", + " TRAIN_COVER_DIR, TRAIN_STEGO_DIR ) \n", + "valid_gen = partial(gen_valid, \\\n", + " VALID_COVER_DIR, VALID_STEGO_DIR)\n", + "\n", + "LOG_DIR = '/media/LogFiles/JUNI_75_04' # path for a log direcotry \n", + "# load_path = LOG_DIR + 'Model_460000.ckpt' # continue training from a specific checkpoint\n", + "load_path=None # training from scratch\n", + "\n", + "if not os.path.exists(LOG_DIR):\n", + " os.makedirs(LOG_DIR)\n", + "\n", + "train_ds_size = len(glob(TRAIN_COVER_DIR + '/*')) * 2\n", + "valid_ds_size = len(glob(VALID_COVER_DIR +'/*')) * 2\n", + "print 'train_ds_size: %i'%train_ds_size\n", + "print 'valid_ds_size: %i'%valid_ds_size\n", + "\n", + "if valid_ds_size % valid_batch_size != 0:\n", + " raise ValueError(\"change batch size for validation\")\n", + " \n", + "optimizer = AdamaxOptimizer\n", + "boundaries = [400000] # learning rate adjustment at iteration 400K\n", + "values = [0.001, 0.0001] # learning rates\n", + "\n", + "train(SRNet, train_gen, valid_gen , train_batch_size, valid_batch_size, valid_ds_size, \\\n", + " optimizer, boundaries, values, train_interval, valid_interval, max_iter,\\\n", + " save_interval, LOG_DIR,num_runner_threads, load_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Testing \n", + "# Cover and Stego directories for testing\n", + "TEST_COVER_DIR = '/media/TST/Cover/'\n", + "TEST_STEGO_DIR = '/media/TST/JUNI_75_04/'\n", + "\n", + "test_batch_size=40\n", + "LOG_DIR = '/media/LogFiles/JUNI_75_04/' \n", + "LOAD_CKPT = LOG_DIR + 'Model_435000.ckpt' # loading from a specific checkpoint\n", + "\n", + "test_gen = partial(gen_valid, \\\n", + " TEST_COVER_DIR, TEST_STEGO_DIR)\n", + "\n", + "test_ds_size = len(glob(TEST_COVER_DIR + '/*')) * 2\n", + "print 'test_ds_size: %i'%test_ds_size\n", + "\n", + "if test_ds_size % test_batch_size != 0:\n", + " raise ValueError(\"change batch size for testing!\")\n", + "\n", + "test_dataset(SRNet, test_gen, test_batch_size, test_ds_size, LOAD_CKPT)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/SRNet/tflib/SCA_SRNet_JPEG.py b/SRNet/tflib/SCA_SRNet_JPEG.py new file mode 100644 index 0000000..8067327 --- /dev/null +++ b/SRNet/tflib/SCA_SRNet_JPEG.py @@ -0,0 +1,133 @@ +import tensorflow as tf +from functools import partial +from tensorflow.contrib import layers +from tensorflow.contrib.framework import arg_scope +import functools +from queues import * +from generator import * +from utils_multistep_lr import * + +class SCA_SRNet(Model): + def _build_model(self, input_batch): + inputs_image, inputs_Beta = tf.split(input_batch, num_or_size_splits=2, axis=3) + if self.data_format == 'NCHW': + reduction_axis = [2,3] + _inputs_image = tf.cast(tf.transpose(inputs_image, [0, 3, 1, 2]), tf.float32) + _inputs_Beta = tf.cast(tf.transpose(inputs_Beta, [0, 3, 1, 2]), tf.float32) + else: + reduction_axis = [1,2] + _inputs_image = tf.cast(inputs_image, tf.float32) + _inputs_Beta = tf.cast(inputs_Beta, tf.float32) + with arg_scope([layers.conv2d], num_outputs=16, + kernel_size=3, stride=1, padding='SAME', + data_format=self.data_format, + activation_fn=None, + weights_initializer=layers.variance_scaling_initializer(), + weights_regularizer=layers.l2_regularizer(2e-4), + biases_initializer=tf.constant_initializer(0.2), + biases_regularizer=None),\ + arg_scope([layers.batch_norm], + decay=0.9, center=True, scale=True, + updates_collections=None, is_training=self.is_training, + fused=True, data_format=self.data_format),\ + arg_scope([layers.avg_pool2d], + kernel_size=[3,3], stride=[2,2], padding='SAME', + data_format=self.data_format): + with tf.variable_scope('Layer1'): # 256*256 + W = tf.get_variable('W', shape=[3,3,1,64],\ + initializer=layers.variance_scaling_initializer(), \ + dtype=tf.float32, \ + regularizer=layers.l2_regularizer(5e-4)) + b = tf.get_variable('b', shape=[64], dtype=tf.float32, \ + initializer=tf.constant_initializer(0.2)) + conv = tf.nn.bias_add( \ + tf.nn.conv2d(tf.cast(_inputs_image, tf.float32), \ + W, [1,1,1,1], 'SAME', \ + data_format=self.data_format), b, \ + data_format=self.data_format, name='Layer1') + actv=tf.nn.relu(conv) + prob_map = tf.sqrt(tf.nn.conv2d(tf.cast(_inputs_Beta, tf.float32), \ + tf.abs(W), [1,1,1,1], 'SAME', \ + data_format=self.data_format)) + out_L1=tf.add_n([actv,prob_map]) + with tf.variable_scope('Layer2'): # 256*256 + conv=layers.conv2d(out_L1) + actv=tf.nn.relu(layers.batch_norm(conv)) + with tf.variable_scope('Layer3'): # 256*256 + conv1=layers.conv2d(actv) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn2=layers.batch_norm(conv2) + res= tf.add(actv, bn2) + with tf.variable_scope('Layer4'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn2=layers.batch_norm(conv2) + res= tf.add(res, bn2) + with tf.variable_scope('Layer5'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer6'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer7'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer8'): # 256*256 + convs = layers.conv2d(res, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer9'): # 128*128 + convs = layers.conv2d(res, num_outputs=64, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=64) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=64) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer10'): # 64*64 + convs = layers.conv2d(res, num_outputs=128, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=128) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=128) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer11'): # 32*32 + convs = layers.conv2d(res, num_outputs=256, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=256) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=256) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer12'): # 16*16 + conv1=layers.conv2d(res, num_outputs=512) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=512) + bn=layers.batch_norm(conv2) + avgp = tf.reduce_mean(bn, reduction_axis, keep_dims=True ) + ip=layers.fully_connected(layers.flatten(avgp), num_outputs=2, + activation_fn=None, normalizer_fn=None, + weights_initializer=tf.random_normal_initializer(mean=0., stddev=0.01), + biases_initializer=tf.constant_initializer(0.), scope='ip') + self.outputs = ip + return self.outputs \ No newline at end of file diff --git a/SRNet/tflib/SCA_SRNet_Spatial.py b/SRNet/tflib/SCA_SRNet_Spatial.py new file mode 100644 index 0000000..8ca098b --- /dev/null +++ b/SRNet/tflib/SCA_SRNet_Spatial.py @@ -0,0 +1,133 @@ +import tensorflow as tf +from functools import partial +from tensorflow.contrib import layers +from tensorflow.contrib.framework import arg_scope +import functools +from tflib.queues import * +from tflib.generator import * +from tflib.utils_multistep_lr import * + +class SCA_SRNet(Model): + def _build_model(self, input_batch): + inputs_image, inputs_Beta = tf.split(input_batch, num_or_size_splits=2, axis=3) + if self.data_format == 'NCHW': + reduction_axis = [2,3] + _inputs_image = tf.cast(tf.transpose(inputs_image, [0, 3, 1, 2]), tf.float32) + _inputs_Beta = tf.cast(tf.transpose(inputs_Beta, [0, 3, 1, 2]), tf.float32) + else: + reduction_axis = [1,2] + _inputs_image = tf.cast(inputs_image, tf.float32) + _inputs_Beta = tf.cast(inputs_Beta, tf.float32) + with arg_scope([layers.conv2d], num_outputs=16, + kernel_size=3, stride=1, padding='SAME', + data_format=self.data_format, + activation_fn=None, + weights_initializer=layers.variance_scaling_initializer(), + weights_regularizer=layers.l2_regularizer(2e-4), + biases_initializer=tf.constant_initializer(0.2), + biases_regularizer=None),\ + arg_scope([layers.batch_norm], + decay=0.9, center=True, scale=True, + updates_collections=None, is_training=self.is_training, + fused=True, data_format=self.data_format),\ + arg_scope([layers.avg_pool2d], + kernel_size=[3,3], stride=[2,2], padding='SAME', + data_format=self.data_format): + with tf.variable_scope('Layer1'): # 256*256 + W = tf.get_variable('W', shape=[3,3,1,64],\ + initializer=layers.variance_scaling_initializer(), \ + dtype=tf.float32, \ + regularizer=layers.l2_regularizer(5e-4)) + b = tf.get_variable('b', shape=[64], dtype=tf.float32, \ + initializer=tf.constant_initializer(0.2)) + conv = tf.nn.bias_add( \ + tf.nn.conv2d(tf.cast(_inputs_image, tf.float32), \ + W, [1,1,1,1], 'SAME', \ + data_format=self.data_format), b, \ + data_format=self.data_format, name='Layer1') + actv=tf.nn.relu(conv) + prob_map = tf.nn.conv2d(tf.cast(_inputs_Beta, tf.float32), \ + tf.abs(W), [1,1,1,1], 'SAME', \ + data_format=self.data_format) + out_L1=tf.add_n([actv,prob_map]) + with tf.variable_scope('Layer2'): # 256*256 + conv=layers.conv2d(out_L1) + actv=tf.nn.relu(layers.batch_norm(conv)) + with tf.variable_scope('Layer3'): # 256*256 + conv1=layers.conv2d(actv) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn2=layers.batch_norm(conv2) + res= tf.add(actv, bn2) + with tf.variable_scope('Layer4'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn2=layers.batch_norm(conv2) + res= tf.add(res, bn2) + with tf.variable_scope('Layer5'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer6'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer7'): # 256*256 + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer8'): # 256*256 + convs = layers.conv2d(res, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer9'): # 128*128 + convs = layers.conv2d(res, num_outputs=64, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=64) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=64) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer10'): # 64*64 + convs = layers.conv2d(res, num_outputs=128, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=128) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=128) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer11'): # 32*32 + convs = layers.conv2d(res, num_outputs=256, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=256) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=256) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer12'): # 16*16 + conv1=layers.conv2d(res, num_outputs=512) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=512) + bn=layers.batch_norm(conv2) + avgp = tf.reduce_mean(bn, reduction_axis, keep_dims=True ) + ip=layers.fully_connected(layers.flatten(avgp), num_outputs=2, + activation_fn=None, normalizer_fn=None, + weights_initializer=tf.random_normal_initializer(mean=0., stddev=0.01), + biases_initializer=tf.constant_initializer(0.), scope='ip') + self.outputs = ip + return self.outputs \ No newline at end of file diff --git a/SRNet/tflib/SRNet.py b/SRNet/tflib/SRNet.py new file mode 100644 index 0000000..50453af --- /dev/null +++ b/SRNet/tflib/SRNet.py @@ -0,0 +1,117 @@ +import tensorflow as tf +from functools import partial +from tensorflow.contrib import layers +from tensorflow.contrib.framework import arg_scope +import functools +from tflib.queues import * +from tflib.generator import * +from tflib.utils_multistep_lr import * + +class SRNet(Model): + def _build_model(self, inputs): + self.inputs = inputs + if self.data_format == 'NCHW': + reduction_axis = [2,3] + _inputs = tf.cast(tf.transpose(inputs, [0, 3, 1, 2]), tf.float32) + else: + reduction_axis = [1,2] + _inputs = tf.cast(inputs, tf.float32) + with arg_scope([layers.conv2d], num_outputs=16, + kernel_size=3, stride=1, padding='SAME', + data_format=self.data_format, + activation_fn=None, + weights_initializer=layers.variance_scaling_initializer(), + weights_regularizer=layers.l2_regularizer(2e-4), + biases_initializer=tf.constant_initializer(0.2), + biases_regularizer=None),\ + arg_scope([layers.batch_norm], + decay=0.9, center=True, scale=True, + updates_collections=None, is_training=self.is_training, + fused=True, data_format=self.data_format),\ + arg_scope([layers.avg_pool2d], + kernel_size=[3,3], stride=[2,2], padding='SAME', + data_format=self.data_format): + with tf.variable_scope('Layer1'): + conv=layers.conv2d(_inputs, num_outputs=64, kernel_size=3) + actv=tf.nn.relu(layers.batch_norm(conv)) + with tf.variable_scope('Layer2'): + conv=layers.conv2d(actv) + actv=tf.nn.relu(layers.batch_norm(conv)) + with tf.variable_scope('Layer3'): + conv1=layers.conv2d(actv) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn2=layers.batch_norm(conv2) + res= tf.add(actv, bn2) + with tf.variable_scope('Layer4'): + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn2=layers.batch_norm(conv2) + res= tf.add(res, bn2) + with tf.variable_scope('Layer5'): + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer6'): + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer7'): + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + res= tf.add(res, bn) + with tf.variable_scope('Layer8'): + convs = layers.conv2d(res, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer9'): + convs = layers.conv2d(res, num_outputs=64, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=64) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=64) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer10'): + convs = layers.conv2d(res, num_outputs=128, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=128) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=128) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer11'): + convs = layers.conv2d(res, num_outputs=256, kernel_size=1, stride=2) + convs = layers.batch_norm(convs) + conv1=layers.conv2d(res, num_outputs=256) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=256) + bn=layers.batch_norm(conv2) + pool = layers.avg_pool2d(bn) + res= tf.add(convs, pool) + with tf.variable_scope('Layer12'): + conv1=layers.conv2d(res, num_outputs=512) + actv1=tf.nn.relu(layers.batch_norm(conv1)) + conv2=layers.conv2d(actv1, num_outputs=512) + bn=layers.batch_norm(conv2) + avgp = tf.reduce_mean(bn, reduction_axis, keep_dims=True ) + ip=layers.fully_connected(layers.flatten(avgp), num_outputs=2, + activation_fn=None, normalizer_fn=None, + weights_initializer=tf.random_normal_initializer(mean=0., stddev=0.01), + biases_initializer=tf.constant_initializer(0.), scope='ip') + self.outputs = ip + return self.outputs \ No newline at end of file diff --git a/SRNet/tflib/generator.py b/SRNet/tflib/generator.py new file mode 100644 index 0000000..a6f58ee --- /dev/null +++ b/SRNet/tflib/generator.py @@ -0,0 +1,81 @@ +import numpy as np +from scipy import misc, io +from glob import glob +import random +# from itertools import izip +from random import random as rand +from random import shuffle + +def gen_flip_and_rot(cover_dir, stego_dir, thread_idx, n_threads): + cover_list = sorted(glob(cover_dir + '/*')) + # print(cover_list) + stego_list = sorted(glob(stego_dir + '/*')) + nb_data = len(cover_list) + assert len(stego_list) != 0, "the stego directory '%s' is empty" % stego_dir + assert nb_data != 0, "the cover directory '%s' is empty" % cover_dir + assert len(stego_list) == nb_data, "the cover directory and " + \ + "the stego directory don't " + \ + "have the same number of files " + \ + "respectively %d and %d" % (nb_data, + \ + len(stego_list)) + load_mat=cover_list[0].endswith('.mat') + if load_mat: + img = io.loadmat(cover_list[0])['im'] + img_shape = img.shape + batch = np.empty((2,img_shape[0],img_shape[1],1), dtype='float32') + else: + img = misc.imread(cover_list[0]) + img_shape = img.shape + batch = np.empty((2,img_shape[0],img_shape[1],1), dtype='uint8') + + iterable = list(zip(cover_list, stego_list)) + while True: + shuffle(iterable) + print("shuffling……") + for cover_path, stego_path in iterable: + if load_mat: + batch[0,:,:,0] = io.loadmat(cover_path)['im'] + batch[1,:,:,0] = io.loadmat(stego_path)['im'] + else: + batch[0,:,:,0] = misc.imread(cover_path) + batch[1,:,:,0] = misc.imread(stego_path) + rot = random.randint(0,3) + if rand() < 0.5: + yield [np.rot90(batch, rot, axes=[1,2]), np.array([0,1], dtype='uint8')] + else: + yield [np.flip(np.rot90(batch, rot, axes=[1,2]), axis=2), np.array([0,1], dtype='uint8')] + + +def gen_valid(cover_dir, stego_dir, thread_idx, n_threads): + cover_list = sorted(glob(cover_dir + '/*')) + stego_list = sorted(glob(stego_dir + '/*')) + nb_data = len(cover_list) + assert len(stego_list) != 0, "the stego directory '%s' is empty" % stego_dir + assert nb_data != 0, "the cover directory '%s' is empty" % cover_dir + assert len(stego_list) == nb_data, "the cover directory and " + \ + "the stego directory don't " + \ + "have the same number of files " + \ + "respectively %d and %d" % (nb_data, \ + len(stego_list)) + load_mat=cover_list[0].endswith('.mat') + if load_mat: + img = io.loadmat(cover_list[0])['im'] + img_shape = img.shape + batch = np.empty((2,img_shape[0],img_shape[1],1), dtype='float32') + else: + img = misc.imread(cover_list[0]) + img_shape = img.shape + batch = np.empty((2,img_shape[0],img_shape[1],1), dtype='uint8') + img_shape = img.shape + + labels = np.array([0, 1], dtype='uint8') + while True: + for cover_path, stego_path in zip(cover_list, stego_list): + if load_mat: + batch[0,:,:,0] = io.loadmat(cover_path)['im'] + batch[1,:,:,0] = io.loadmat(stego_path)['im'] + else: + batch[0,:,:,0] = misc.imread(cover_path) + batch[1,:,:,0] = misc.imread(stego_path) + # print([batch,labels]) + yield [batch, labels] diff --git a/SRNet/tflib/queues.py b/SRNet/tflib/queues.py new file mode 100644 index 0000000..8b35119 --- /dev/null +++ b/SRNet/tflib/queues.py @@ -0,0 +1,68 @@ +import tensorflow as tf +import threading + +class GeneratorRunner(): + """ + This class manage a multithreaded queue filled with a generator + """ + def __init__(self, generator, capacity): + """ + inputs: generator feeding the data, must have thread_idx + as parameter (but the parameter may be not used) + """ + self.generator = generator + _input = generator(0,1).__next__() + if type(_input) is not list: + raise ValueError("generator doesn't return" \ + "a list: %r" % type(_input)) + input_batch_size = _input[0].shape[0] + if not all(_input[i].shape[0] == input_batch_size for i in range(len(_input))): + raise ValueError("all the inputs doesn't have the same batch size,"\ + "the batch sizes are: %s" % [_input[i].shape[0] for i in range(len(_input))]) + self.data = [] + self.dtypes = [] + self.shapes = [] + for i in range(len(_input)): + self.shapes.append(_input[i].shape[1:]) + self.dtypes.append(_input[i].dtype) + self.data.append(tf.placeholder(dtype=self.dtypes[i], \ + shape=(input_batch_size,) + self.shapes[i])) + self.queue = tf.FIFOQueue(capacity, shapes=self.shapes, \ + dtypes=self.dtypes) + self.enqueue_op = self.queue.enqueue_many(self.data) + self.close_queue_op = self.queue.close(cancel_pending_enqueues=True) + + def get_batched_inputs(self, batch_size): + """ + Return tensors containing a batch of generated data + """ + batch = self.queue.dequeue_many(batch_size) + return batch + + def thread_main(self, sess, thread_idx=0, n_threads=1): + try: + for data in self.generator(thread_idx, n_threads): + sess.run(self.enqueue_op, feed_dict={i: d \ + for i, d in zip(self.data, data)}) + if self.stop_threads: + return + except RuntimeError: + pass + + def start_threads(self, sess, n_threads=1): + self.stop_threads = False + self.threads = [] + for n in range(n_threads): + t = threading.Thread(target=self.thread_main, args=(sess, n, n_threads)) + t.daemon = True + t.start() + self.threads.append(t) + return self.threads + + def stop_runner(self, sess): + self.stop_threads = True + sess.run(self.close_queue_op) + +def queueSelection(runners, sel, batch_size): + selection_queue = tf.FIFOQueue.from_list(sel, [r.queue for r in runners]) + return selection_queue.dequeue_many(batch_size) \ No newline at end of file diff --git a/SRNet/tflib/utils_multistep_lr.py b/SRNet/tflib/utils_multistep_lr.py new file mode 100644 index 0000000..3509423 --- /dev/null +++ b/SRNet/tflib/utils_multistep_lr.py @@ -0,0 +1,231 @@ +import tensorflow as tf +import time +from tflib.queues import * +from tflib.generator import * + +class average_summary: + def __init__(self, variable, name, num_iterations): + self.sum_variable = tf.get_variable(name, shape=[], \ + initializer=tf.constant_initializer(0), \ + dtype=variable.dtype.base_dtype, \ + trainable=False, \ + collections=[tf.GraphKeys.LOCAL_VARIABLES]) + with tf.control_dependencies([variable]): + self.increment_op = tf.assign_add(self.sum_variable, variable) + self.mean_variable = self.sum_variable / float(num_iterations) + self.summary = tf.summary.scalar(name, self.mean_variable) + with tf.control_dependencies([self.summary]): + self.reset_variable_op = tf.assign(self.sum_variable, 0) + + def add_summary(self, sess, writer, step): + s, _ = sess.run([self.summary, self.reset_variable_op]) + writer.add_summary(s, step) + +class Model: + def __init__(self, is_training=None, data_format='NCHW'): + self.data_format = data_format + if is_training is None: + self.is_training = tf.get_variable('is_training', dtype=tf.bool, \ + initializer=tf.constant_initializer(True), \ + trainable=False) + else: + self.is_training = is_training + + def _build_model(self, inputs): + raise NotImplementedError('Here is your model definition') + + def _build_losses(self, labels): + self.labels = tf.cast(labels, tf.int64) + with tf.variable_scope('loss'): + oh = tf.one_hot(self.labels, 2) + xen_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( \ + labels=oh,logits=self.outputs)) + reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) + self.loss = tf.add_n([xen_loss] + reg_losses) + with tf.variable_scope('accuracy'): + am = tf.argmax(self.outputs, 1) + equal = tf.equal(am, self.labels) + self.accuracy = tf.reduce_mean(tf.cast(equal, tf.float32)) + return self.loss, self.accuracy + +def train(model_class, train_gen, valid_gen, train_batch_size, \ + valid_batch_size, valid_ds_size, optimizer, boundaries, values, \ + train_interval, valid_interval, max_iter, \ + save_interval, log_path, num_runner_threads=1, \ + load_path=None): + tf.reset_default_graph() + train_runner = GeneratorRunner(train_gen, train_batch_size * 10) + valid_runner = GeneratorRunner(valid_gen, valid_batch_size * 10) + is_training = tf.get_variable('is_training', dtype=tf.bool, \ + initializer=True, trainable=False) + if train_batch_size == valid_batch_size: + batch_size = train_batch_size + disable_training_op = tf.assign(is_training, False) + enable_training_op = tf.assign(is_training, True) + else: + batch_size = tf.get_variable('batch_size', dtype=tf.int32, \ + initializer=train_batch_size, \ + trainable=False, \ + collections=[tf.GraphKeys.LOCAL_VARIABLES]) + disable_training_op = tf.group(tf.assign(is_training, False), \ + tf.assign(batch_size, valid_batch_size)) + enable_training_op = tf.group(tf.assign(is_training, True), \ + tf.assign(batch_size, train_batch_size)) + img_batch, label_batch = queueSelection([valid_runner, train_runner], \ + tf.cast(is_training, tf.int32), \ + batch_size) + model = model_class(is_training, 'NCHW') + model._build_model(img_batch) + loss, accuracy = model._build_losses(label_batch) + train_loss_s = average_summary(loss, 'train_loss', train_interval) + train_accuracy_s = average_summary(accuracy, 'train_accuracy', \ + train_interval) + valid_loss_s = average_summary(loss, 'valid_loss', \ + float(valid_ds_size) / float(valid_batch_size)) + valid_accuracy_s = average_summary(accuracy, 'valid_accuracy', \ + float(valid_ds_size) / float(valid_batch_size)) + global_step = tf.get_variable('global_step', dtype=tf.int32, shape=[], \ + initializer=tf.constant_initializer(0), \ + trainable=False) + + learning_rate = tf.train.piecewise_constant(global_step, boundaries, values) + lr_summary = tf.summary.scalar('learning_rate', learning_rate) + optimizer = optimizer(learning_rate) + + minimize_op = optimizer.minimize(loss, global_step) + train_op = tf.group(minimize_op, train_loss_s.increment_op, \ + train_accuracy_s.increment_op) + increment_valid = tf.group(valid_loss_s.increment_op, \ + valid_accuracy_s.increment_op) + init_op = tf.group(tf.global_variables_initializer(), \ + tf.local_variables_initializer()) + saver = tf.train.Saver(max_to_keep=10000) + with tf.Session() as sess: + sess.run(init_op) + if load_path is not None: + saver.restore(sess, load_path) + train_runner.start_threads(sess, num_runner_threads) + valid_runner.start_threads(sess, 1) + writer = tf.summary.FileWriter(log_path + '/LogFile/', \ + sess.graph) + start = sess.run(global_step) + sess.run(disable_training_op) + sess.run([valid_loss_s.reset_variable_op, \ + valid_accuracy_s.reset_variable_op, \ + train_loss_s.reset_variable_op, \ + train_accuracy_s.reset_variable_op]) + _time = time.time() + for j in range(0, valid_ds_size, valid_batch_size): + sess.run([increment_valid]) + _acc_val = sess.run(valid_accuracy_s.mean_variable) + print ("initial accuracy on validation set:", _acc_val) + print ("evaluation time on validation set:", time.time() - _time, "seconds") + valid_accuracy_s.add_summary(sess, writer, start) + valid_loss_s.add_summary(sess, writer, start) + sess.run(enable_training_op) + print ("network will be evaluatd every %i iterations on validation set" %valid_interval) + for i in range(start+1, max_iter+1): + sess.run(train_op) + if i % train_interval == 0: + train_loss_s.add_summary(sess, writer, i) + train_accuracy_s.add_summary(sess, writer, i) + s = sess.run(lr_summary) + writer.add_summary(s, i) + if i % valid_interval == 0: + sess.run(disable_training_op) + for j in range(0, valid_ds_size, valid_batch_size): + sess.run([increment_valid]) + valid_loss_s.add_summary(sess, writer, i) + valid_accuracy_s.add_summary(sess, writer, i) + sess.run(enable_training_op) + if i % save_interval == 0: + saver.save(sess, log_path + '/Model_' + str(i) + '.ckpt') + +def test_dataset(model_class, gen, batch_size, ds_size, load_path): + tf.reset_default_graph() + runner = GeneratorRunner(gen, batch_size * 10) + img_batch, label_batch = runner.get_batched_inputs(batch_size) + model = model_class(False, 'NCHW') + model._build_model(img_batch) + loss, accuracy = model._build_losses(label_batch) + loss_summary = average_summary(loss, 'loss', \ + float(ds_size) / float(batch_size)) + accuracy_summary = average_summary(accuracy, 'accuracy', \ + float(ds_size) / float(batch_size)) + increment_op = tf.group(loss_summary.increment_op, \ + accuracy_summary.increment_op) + global_step = tf.get_variable('global_step', dtype=tf.int32, shape=[], \ + initializer=tf.constant_initializer(0), \ + trainable=False) + init_op = tf.group(tf.global_variables_initializer(), \ + tf.local_variables_initializer()) + saver = tf.train.Saver(max_to_keep=10000) + with tf.Session() as sess: + sess.run(init_op) + saver.restore(sess, load_path) + runner.start_threads(sess, 1) + for j in range(0, ds_size, batch_size): + sess.run(increment_op) + mean_loss, mean_accuracy = sess.run([loss_summary.mean_variable ,\ + accuracy_summary.mean_variable]) + print ("Accuracy:", mean_accuracy, " | Loss:", mean_loss) + +### Implementation of Adamax optimizer, taken from : https://github.com/openai/iaf/blob/master/tf_utils/adamax.py +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import state_ops +from tensorflow.python.framework import ops +from tensorflow.python.training import optimizer +import tensorflow as tf + +class AdamaxOptimizer(optimizer.Optimizer): + """ + Optimizer that implements the Adamax algorithm. + See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980) + ([pdf](http://arxiv.org/pdf/1412.6980.pdf)). + @@__init__ + """ + + def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, use_locking=False, name="Adamax"): + super(AdamaxOptimizer, self).__init__(use_locking, name) + self._lr = learning_rate + self._beta1 = beta1 + self._beta2 = beta2 + + # Tensor versions of the constructor arguments, created in _prepare(). + self._lr_t = None + self._beta1_t = None + self._beta2_t = None + + def _prepare(self): + self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate") + self._beta1_t = ops.convert_to_tensor(self._beta1, name="beta1") + self._beta2_t = ops.convert_to_tensor(self._beta2, name="beta2") + + def _create_slots(self, var_list): + # Create slots for the first and second moments. + for v in var_list: + self._zeros_slot(v, "m", self._name) + self._zeros_slot(v, "v", self._name) + + def _apply_dense(self, grad, var): + lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) + beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) + beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) + if var.dtype.base_dtype == tf.float16: + eps = 1e-7 # Can't use 1e-8 due to underflow -- not sure if it makes a big difference. + else: + eps = 1e-8 + + v = self.get_slot(var, "v") + v_t = v.assign(beta1_t * v + (1. - beta1_t) * grad) + m = self.get_slot(var, "m") + m_t = m.assign(tf.maximum(beta2_t * m + eps, tf.abs(grad))) + g_t = v_t / m_t + + var_update = state_ops.assign_sub(var, lr_t * g_t) + return control_flow_ops.group(*[var_update, m_t, v_t]) + + def _apply_sparse(self, grad, var): + raise NotImplementedError("Sparse gradient updates are not supported.") + diff --git a/YeNet-Tensorflow/README.md b/YeNet-Tensorflow/README.md new file mode 100644 index 0000000..68156cd --- /dev/null +++ b/YeNet-Tensorflow/README.md @@ -0,0 +1,8 @@ +# YeNet-Tensorflow +Tensorflow implementation of "Deep Learning Hierarchical Representation for Image Steganalysis" by Jian Ye, Jiangqun Ni and Yang Yi + +## Dataset +Training and validation contains there own cover and stego / beta maps images directory, stego images must have the same name than there corresponding cover + +## Publication +[The publication can be found here](http://ieeexplore.ieee.org/document/7937836/) diff --git a/YeNet-Tensorflow/SRM_Kernels.npy b/YeNet-Tensorflow/SRM_Kernels.npy new file mode 100644 index 0000000..cea98d3 Binary files /dev/null and b/YeNet-Tensorflow/SRM_Kernels.npy differ diff --git a/YeNet-Tensorflow/YeNet.py b/YeNet-Tensorflow/YeNet.py new file mode 100644 index 0000000..696df72 --- /dev/null +++ b/YeNet-Tensorflow/YeNet.py @@ -0,0 +1,119 @@ +import tensorflow as tf +from tensorflow.contrib import layers +from tensorflow.contrib.framework import add_arg_scope, arg_scope, arg_scoped_arguments +import layers as my_layers +from utils import * + +SRM_Kernels = np.load('SRM_Kernels.npy') + +class YeNet(Model): + def __init__(self, is_training=None, data_format='NCHW', \ + with_bn=False, tlu_threshold=3): + super(YeNet, self).__init__(is_training=is_training, \ + data_format=data_format) + self.with_bn = with_bn + self.tlu_threshold = tlu_threshold + + def _build_model(self, inputs): + self.inputs = inputs + if self.data_format == 'NCHW': + channel_axis = 1 + _inputs = tf.cast(tf.transpose(inputs, [0, 3, 1, 2]), tf.float32) + else: + channel_axis = 3 + _inputs = tf.cast(inputs, tf.float32) + self.L = [] + with arg_scope([layers.avg_pool2d], \ + padding='VALID', data_format=self.data_format): + with tf.variable_scope('SRM_preprocess'): + W_SRM = tf.get_variable('W', initializer=SRM_Kernels, \ + dtype=tf.float32, \ + regularizer=None) + b = tf.get_variable('b', shape=[30], dtype=tf.float32, \ + initializer=tf.constant_initializer(0.)) + self.L.append(tf.nn.bias_add( \ + tf.nn.conv2d(_inputs, \ + W_SRM, [1,1,1,1], 'VALID', \ + data_format=self.data_format), b, \ + data_format=self.data_format, name='Layer1')) + self.L.append(tf.clip_by_value(self.L[-1], \ + -self.tlu_threshold, self.tlu_threshold, \ + name='TLU')) + with tf.variable_scope('ConvNetwork'): + with arg_scope([my_layers.conv2d], num_outputs=30, \ + kernel_size=3, stride=1, padding='VALID', \ + data_format=self.data_format, \ + activation_fn=tf.nn.relu, \ + weights_initializer=layers.xavier_initializer_conv2d(), \ + weights_regularizer=layers.l2_regularizer(5e-4), \ + biases_initializer=tf.constant_initializer(0.2), \ + biases_regularizer=None), arg_scope([layers.batch_norm], \ + decay=0.9, center=True, scale=True, \ + updates_collections=None, is_training=self.is_training, \ + fused=True, data_format=self.data_format): + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm1')) + self.L.append(my_layers.conv2d(self.L[-1], \ + scope='Layer2')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm2')) + self.L.append(my_layers.conv2d(self.L[-1], \ + scope='Layer3')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm3')) + self.L.append(my_layers.conv2d(self.L[-1], \ + scope='Layer4')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm4')) + self.L.append(layers.avg_pool2d(self.L[-1], \ + kernel_size=[2,2], scope='Stride1')) + with arg_scope([my_layers.conv2d], kernel_size=5, \ + num_outputs=32): + self.L.append(my_layers.conv2d(self.L[-1], \ + scope='Layer5')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm5')) + self.L.append(layers.avg_pool2d(self.L[-1], \ + kernel_size=[3,3], \ + scope='Stride2')) + self.L.append(my_layers.conv2d(self.L[-1], \ + scope='Layer6')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm6')) + self.L.append(layers.avg_pool2d(self.L[-1], \ + kernel_size=[3,3], \ + scope='Stride3')) + self.L.append(my_layers.conv2d(self.L[-1], \ + scope='Layer7')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm7')) + self.L.append(layers.avg_pool2d(self.L[-1], \ + kernel_size=[3,3], \ + scope='Stride4')) + self.L.append(my_layers.conv2d(self.L[-1], \ + num_outputs=16, \ + scope='Layer8')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm8')) + self.L.append(my_layers.conv2d(self.L[-1], \ + num_outputs=16, stride=3, \ + scope='Layer9')) + if self.with_bn: + self.L.append(layers.batch_norm(self.L[-1], \ + scope='Norm9')) + self.L.append(layers.flatten(self.L[-1])) + self.L.append(layers.fully_connected(self.L[-1], num_outputs=2, \ + activation_fn=None, normalizer_fn=None, \ + weights_initializer=tf.random_normal_initializer(mean=0., stddev=0.01), \ + biases_initializer=tf.constant_initializer(0.), scope='ip')) + self.outputs = self.L[-1] + return self.outputs + diff --git a/YeNet-Tensorflow/__pycache__/layers.cpython-37.pyc b/YeNet-Tensorflow/__pycache__/layers.cpython-37.pyc new file mode 100644 index 0000000..ea9d1a3 Binary files /dev/null and b/YeNet-Tensorflow/__pycache__/layers.cpython-37.pyc differ diff --git a/YeNet-Tensorflow/generator.py b/YeNet-Tensorflow/generator.py new file mode 100644 index 0000000..131e7d7 --- /dev/null +++ b/YeNet-Tensorflow/generator.py @@ -0,0 +1,178 @@ +import numpy as np +from scipy import misc, io +from glob import glob +import random +from itertools import izip +from random import random as rand +from random import shuffle +import h5py + +def gen_embedding_otf(cover_dir, beta_dir, shuf_pair, \ + thread_idx=0, n_threads=1): + cover_list = sorted(glob(cover_dir + '/*')) + beta_list = sorted(glob(beta_dir + '/*')) + nb_data = len(cover_list) + assert len(beta_list) != 0, "the beta directory '%s' is empty" % beta_dir + assert nb_data != 0, "the cover directory '%s' is empty" % cover_dir + assert len(beta_list) == nb_data, "the cover directory and " + \ + "the beta directory don't " + \ + "have the same number of files " + \ + "respectively %d and %d" % (nb_data, \ + len(beta_list)) + img = misc.imread(cover_list[0]) + img_shape = img.shape + batch = np.empty((2, img_shape[0], img_shape[1], 1), dtype='uint8') + beta_map = np.empty(img_shape, dtype='> 可嵌入",self.available_info_len,'bits') + + def get_sequence_after_dct(self): + return self.sequence_after_dct + + def write(self,info): + """先嵌入信息的长度,然后嵌入信息""" + info=self._set_info_len(info) + info_len=len(info) + info_index=0 + im_index=0 + while True: + if info_index>=info_len: + break + data=info[info_index] + if self._write(im_index,data): + info_index+=1 + im_index+=1 + #print(self.sequence_after_dct) + b={0:0,1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:0,-1:0,-2:0,-3:0,-4:0,-5:0,-6:0,-7:0,-8:0} + for i in self.sequence_after_dct: + if i in b: + b[i]+=1 + print("经过信息隐藏后JPEG的DCT系数变化") + print(b) + + def read(self): + """先读出信息的长度,然后读出信息""" + _len,sequence_index=self._get_info_len() + info=[] + info_index=0 + + while True: + if info_index>=_len: + break + data=self._read(sequence_index) + if data!=None: + info.append(data) + info_index+=1 + sequence_index+=1 + + return info + + #===============================================================# + + def _set_info_len(self,info): + l=int(math.log(self.available_info_len,2))+1 + info_len=[0]*l + _len=len(info) + info_len[-len(bin(_len))+2:]=[int(i) for i in bin(_len)[2:]] + #print(info_len+info) + return info_len+info + + def _get_info_len(self): + l=int(math.log(self.available_info_len,2))+1 + len_list=[] + _l_index=0 + _seq_index=0 + while True: + if _l_index>=l: + break + _d=self._read(_seq_index) + if _d!=None: + len_list.append(str(_d)) + _l_index+=1 + _seq_index+=1 + _len=''.join(len_list) + _len=int(_len,2) + return _len,_seq_index + + # 注意经过DCT会有负值,此处最低有效位的嵌入方式与空域LSB略有不同 + def _write(self,index,data): + origin=self.sequence_after_dct[index] + if origin in (-1,1,0): + return False + + lower_bit=origin%2 + if lower_bit==data: + pass + elif origin>0: + if (lower_bit,data) == (0,1): + self.sequence_after_dct[index]=origin+1 + elif (lower_bit,data) == (1,0): + self.sequence_after_dct[index]=origin-1 + elif origin<0: + if (lower_bit,data) == (0,1): + self.sequence_after_dct[index]=origin-1 + elif (lower_bit,data) == (1,0): + self.sequence_after_dct[index]=origin+1 + + return True + + def _read(self,index): + if self.sequence_after_dct[index] not in (-1,1,0): + return self.sequence_after_dct[index]%2 + else: + return None + + +class F3(Jsteg): + def __init__(self): + Jsteg.__init__(self) + + def set_sequence_after_dct(self,sequence_after_dct): + self.sequence_after_dct=sequence_after_dct + sum_len=len(self.sequence_after_dct) + zero_len=len([i for i in self.sequence_after_dct if i==0]) + one_len=len([i for i in self.sequence_after_dct if i in (-1,1)]) + self.available_info_len=sum_len-zero_len-one_len # 不是特别可靠 + print ("Load>> 大约可嵌入",sum_len-zero_len-int(one_len/2),'bits') + print ("Load>> 最少可嵌入",self.available_info_len,'bits\n') + + def _write(self,index,data): + origin=self.sequence_after_dct[index] + if origin == 0: + return False + elif origin in (-1,1) and data==0: + self.sequence_after_dct[index]=0 + return False + + lower_bit=origin%2 + + if lower_bit==data: + pass + elif origin>0: + self.sequence_after_dct[index]=origin-1 + elif origin<0: + self.sequence_after_dct[index]=origin+1 + return True + + def _read(self,index): + if self.sequence_after_dct[index] != 0: + return self.sequence_after_dct[index]%2 + else: + return None + + +class F4(Jsteg): + def __init__(self): + Jsteg.__init__(self) + + def set_sequence_after_dct(self,sequence_after_dct): + self.sequence_after_dct=sequence_after_dct + sum_len=len(self.sequence_after_dct) + zero_len=len([i for i in self.sequence_after_dct if i==0]) + one_len=len([i for i in self.sequence_after_dct if i in (-1,1)]) + self.available_info_len=sum_len-zero_len-one_len # 不是特别可靠 + print ("Load>> 大约可嵌入",sum_len-zero_len-int(one_len/2),'bits') + print ("Load>> 最少可嵌入",self.available_info_len,'bits\n') + + def _write(self,index,data): + origin=self.sequence_after_dct[index] + if origin == 0: + return False + elif origin == 1 and data==0: + self.sequence_after_dct[index]=0 + return False + + elif origin == -1 and data==1: + self.sequence_after_dct[index]=0 + return False + + lower_bit=origin%2 + + if origin >0: + if lower_bit!=data: + self.sequence_after_dct[index]=origin-1 + else: + if lower_bit==data: + self.sequence_after_dct[index]=origin+1 + return True + + + def _read(self,index): + if self.sequence_after_dct[index] >0: + return self.sequence_after_dct[index]%2 + elif self.sequence_after_dct[index]<0: + return (self.sequence_after_dct[index]+1)%2 + else: + return None + + + + +if __name__=="__main__": + jsteg=Jsteg() + f3=F3() + f4=F4() + # 写 + sequence_after_dct=DCT.after_dct + jsteg.set_sequence_after_dct(sequence_after_dct) + f3.set_sequence_after_dct(sequence_after_dct) + f4.set_sequence_after_dct(sequence_after_dct) + a={0:0,1:0,2:0,3:0,4:0,5:0,6:0,7:0,8:0,-1:0,-2:0,-3:0,-4:0,-5:0,-6:0,-7:0,-8:0} + for i in sequence_after_dct: + if i in a: + a[i]+=1 + print("JPEG的DCT系数") + print(a) + info1=[int(i+0.5) for i in np.random.rand(200000)] + + print("Jsteg begin writing!") + print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") + jsteg.write(info1) + + print("F3steg begin writing!") + print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") + f3.write(info1) + print("F4steg begin writing!") + print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") + f4.write(info1) + ''' + # 读 + sequence_after_dct2=jsteg.get_sequence_after_dct() + sequence_after_dct3=f3.get_sequence_after_dct() + sequence_after_dct4=f4.get_sequence_after_dct() + jsteg.set_sequence_after_dct(sequence_after_dct2) + f3.set_sequence_after_dct(sequence_after_dct3) + f4.set_sequence_after_dct(sequence_after_dct4) + info2=jsteg.read() + info3=f3.read() + info4=f4.read() + ''' diff --git a/富模型空域图像隐写分析(SRM)/提取特征/1.pgm b/富模型空域图像隐写分析(SRM)/提取特征/1.pgm new file mode 100644 index 0000000..3bf4834 --- /dev/null +++ b/富模型空域图像隐写分析(SRM)/提取特征/1.pgm @@ -0,0 +1,4 @@ +P5 +512 512 +255 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\ No newline at end of file diff --git a/富模型空域图像隐写分析(SRM)/提取特征/SRM.m b/富模型空域图像隐写分析(SRM)/提取特征/SRM.m new file mode 100644 index 0000000..aa34839 --- /dev/null +++ b/富模型空域图像隐写分析(SRM)/提取特征/SRM.m @@ -0,0 +1,1021 @@ +function f = SRM(IMAGE) +% ------------------------------------------------------------------------- +% Copyright (c) 2011 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: jan@kodovsky.com | fridrich@binghamton.edu | October 2011 +% http://dde.binghamton.edu/download/feature_extractors +% ------------------------------------------------------------------------- +% Extracts all 106 submodels presented in [1] as part of a rich model for +% steganalysis of digital images. All features are calculated in the +% spatial domain and are stored in a structured variable 'f'. For more +% deatils about the individual submodels, please see the publication [1]. +% Total dimensionality of all 106 submodels is 34671. +% ------------------------------------------------------------------------- +% Input: IMAGE ... path to the image (can be JPEG) +% Output: f ....... extracted SRM features in a structured format +% ------------------------------------------------------------------------- +% [1] Rich Models for Steganalysis of Digital Images, J. Fridrich and J. +% Kodovsky, IEEE Transactions on Information Forensics and Security, 2011. +% Under review. +% ------------------------------------------------------------------------- + +X = double(imread(IMAGE)); + +f = post_processing(all1st(X,1),'f1',1); % 1st order, q=1 +f = post_processing(all1st(X,2),'f1',2,f); % 1st order, q=2 +for q=[1 1.5 2], f = post_processing(all2nd(X,q*2),'f2',q,f); end % 2nd order +for q=[1 1.5 2], f = post_processing(all3rd(X,q*3),'f3',q,f); end % 3rd order +for q=[1 1.5 2], f = post_processing(all3x3(X,q*4),'f3x3',q,f); end % 3x3 +for q=[1 1.5 2], f = post_processing(all5x5(X,q*12),'f5x5',q,f); end % 5x5 + +function RESULT = post_processing(DATA,f,q,RESULT) + +Ss = fieldnames(DATA); +for Sid = 1:length(Ss) + VARNAME = [f '_' Ss{Sid} '_q' strrep(num2str(q),'.','')]; + eval(['RESULT.' VARNAME ' = reshape(single(DATA.' Ss{Sid} '),1,[]);' ]) +end + +% symmetrize +L = fieldnames(RESULT); +for i=1:length(L) + name = L{i}; % feature name + if name(1)=='s', continue; end + [T,N,Q] = parse_feaname(name); + if strcmp(T,''), continue; end + % symmetrization + if strcmp(N(1:3),'min') || strcmp(N(1:3),'max') + % minmax symmetrization + OUT = ['s' T(2:end) '_minmax' N(4:end) '_' Q]; + if isfield(RESULT,OUT), continue; end + eval(['Fmin = RESULT.' strrep(name,'max','min') ';']); + eval(['Fmax = RESULT.' strrep(name,'min','max') ';']); + F = symfea([Fmin Fmax]',2,4,'mnmx')'; %#ok<*NASGU> + eval(['RESULT.' OUT ' = single(F);' ]); + elseif strcmp(N(1:4),'spam') + % spam symmetrization + OUT = ['s' T(2:end) '_' N '_' Q]; + if isfield(RESULT,OUT), continue; end + eval(['Fold = RESULT.' name ';']); + F = symm1(Fold',2,4)'; + eval(['RESULT.' OUT ' = single(F);' ]); + end +end +% delete RESULT.f* +L = fieldnames(RESULT); +for i=1:length(L) + name = L{i}; % feature name + if name(1)=='f' + RESULT = rmfield(RESULT,name); + end +end +% merge spam features +L = fieldnames(RESULT); +for i=1:length(L) + name = L{i}; % feature name + [T,N,Q] = parse_feaname(name); + if ~strcmp(N(1:4),'spam'), continue; end + if strcmp(T,''), continue; end + if strcmp(N(end),'v')||(strcmp(N,'spam11')&&strcmp(T,'s5x5')) + elseif strcmp(N(end),'h') + % h+v union + OUT = [T '_' N 'v_' Q ]; + if isfield(RESULT,OUT), continue; end + name2 = strrep(name,'h_','v_'); + eval(['Fh = RESULT.' name ';']); + eval(['Fv = RESULT.' name2 ';']); + eval(['RESULT.' OUT ' = [Fh Fv];']); + RESULT = rmfield(RESULT,name); + RESULT = rmfield(RESULT,name2); + elseif strcmp(N,'spam11') + % KBKV creation + OUT = ['s35_' N '_' Q]; + if isfield(RESULT,OUT), continue; end + name1 = strrep(name,'5x5','3x3'); + name2 = strrep(name,'3x3','5x5'); + if ~isfield(RESULT,name1), continue; end + if ~isfield(RESULT,name2), continue; end + eval(['F_KB = RESULT.' name1 ';']); + eval(['F_KV = RESULT.' name2 ';']); + eval(['RESULT.' OUT ' = [F_KB F_KV];']); + RESULT = rmfield(RESULT,name1); + RESULT = rmfield(RESULT,name2); + end +end +function [T,N,Q] = parse_feaname(name) +[T,N,Q] = deal(''); +P = strfind(name,'_'); if length(P)~=2, return; end +T = name(1:P(1)-1); +N = name(P(1)+1:P(2)-1); +Q = name(P(2)+1:end); +function g = all1st(X,q) +% +% X must be a matrix of doubles or singles (the image) and q is the +% quantization step (any positive number). +% +% Recommended values of q are c, 1.5c, 2c, where c is the central +% coefficient in the differential (at X(I,J)). +% +% This function outputs co-occurrences of ALL 1st-order residuals +% listed in Figure 1 in our journal HUGO paper (version from June 14), +% including the naming convention. +% +% List of outputted features: +% +% 1a) spam14h +% 1b) spam14v (orthogonal-spam) +% 1c) minmax22v +% 1d) minmax24 +% 1e) minmax34v +% 1f) minmax41 +% 1g) minmax34 +% 1h) minmax48h +% 1i) minmax54 +% +% Naming convention: +% +% name = {type}{f}{sigma}{scan} +% type \in {spam, minmax} +% f \in {1,2,3,4,5} number of filters that are "minmaxed" +% sigma \in {1,2,3,4,8} symmetry index +% scan \in {h,v,\emptyset} scan of the cooc matrix (empty = sum of both +% h and v scans). +% +% All odd residuals are implemented the same way simply by +% narrowing the range for I and J and replacing the residuals -- +% -- they should "stick out" (trcet) in the same direction as +% the 1st order ones. For example, for the 3rd order: +% +% RU = -X(I-2,J+2)+3*X(I-1,J+1)-3*X(I,J)+X(I+1,J-1); ... etc. +% +% Note1: The term X(I,J) should always have the "-" sign. +% Note2: This script does not include s, so, cout, cin versions (weak). +% This function calls Cooc.m and Quant.m + +[M N] = size(X); [I,J,T,order] = deal(2:M-1,2:N-1,2,4); +% Variable names are self-explanatory (R = right, U = up, L = left, D = down) +[R,L,U,D] = deal(X(I,J+1)-X(I,J),X(I,J-1)-X(I,J),X(I-1,J)-X(I,J),X(I+1,J)-X(I,J)); +[Rq,Lq,Uq,Dq] = deal(Quant(R,q,T),Quant(L,q,T),Quant(U,q,T),Quant(D,q,T)); +[RU,LU,RD,LD] = deal(X(I-1,J+1)-X(I,J),X(I-1,J-1)-X(I,J),X(I+1,J+1)-X(I,J),X(I+1,J-1)-X(I,J)); +[RUq,RDq,LUq,LDq] = deal(Quant(RU,q,T),Quant(RD,q,T),Quant(LU,q,T),Quant(LD,q,T)); +% minmax22h -- to be symmetrized as mnmx, directional, hv-nonsymmetrical. +[RLq_min,UDq_min,RLq_max,UDq_max] = deal(min(Rq,Lq),min(Uq,Dq),max(Rq,Lq),max(Uq,Dq)); +g.min22h = reshape(Cooc(RLq_min,order,'hor',T) + Cooc(UDq_min,order,'ver',T),[],1); +g.max22h = reshape(Cooc(RLq_max,order,'hor',T) + Cooc(UDq_max,order,'ver',T),[],1); +% minmax34h -- to be symmetrized as mnmx, directional, hv-nonsymmetrical +[Uq_min,Rq_min,Dq_min,Lq_min] = deal(min(min(Lq,Uq),Rq),min(min(Uq,Rq),Dq),min(min(Rq,Dq),Lq),min(min(Dq,Lq),Uq)); +[Uq_max,Rq_max,Dq_max,Lq_max] = deal(max(max(Lq,Uq),Rq),max(max(Uq,Rq),Dq),max(max(Rq,Dq),Lq),max(max(Dq,Lq),Uq)); +g.min34h = reshape(Cooc([Uq_min;Dq_min],order,'hor',T) + Cooc([Lq_min Rq_min],order,'ver',T),[],1); +g.max34h = reshape(Cooc([Uq_max;Dq_max],order,'hor',T) + Cooc([Rq_max Lq_max],order,'ver',T),[],1); +% spam14h/v -- to be symmetrized as spam, directional, hv-nonsymmetrical +g.spam14h = reshape(Cooc(Rq,order,'hor',T) + Cooc(Uq,order,'ver',T),[],1); +g.spam14v = reshape(Cooc(Rq,order,'ver',T) + Cooc(Uq,order,'hor',T),[],1); +% minmax22v -- to be symmetrized as mnmx, directional, hv-nonsymmetrical. Good with higher-order residuals! Note: 22h is bad (too much neighborhood overlap). +g.min22v = reshape(Cooc(RLq_min,order,'ver',T) + Cooc(UDq_min,order,'hor',T),[],1); +g.max22v = reshape(Cooc(RLq_max,order,'ver',T) + Cooc(UDq_max,order,'hor',T),[],1); +% minmax24 -- to be symmetrized as mnmx, directional, hv-symmetrical. Darn good, too. +[RUq_min,RDq_min,LUq_min,LDq_min] = deal(min(Rq,Uq),min(Rq,Dq),min(Lq,Uq),min(Lq,Dq)); +[RUq_max,RDq_max,LUq_max,LDq_max] = deal(max(Rq,Uq),max(Rq,Dq),max(Lq,Uq),max(Lq,Dq)); +g.min24 = reshape(Cooc([RUq_min;RDq_min;LUq_min;LDq_min],order,'hor',T) + Cooc([RUq_min RDq_min LUq_min LDq_min],order,'ver',T),[],1); +g.max24 = reshape(Cooc([RUq_max;RDq_max;LUq_max;LDq_max],order,'hor',T) + Cooc([RUq_max RDq_max LUq_max LDq_max],order,'ver',T),[],1); +% minmax34v -- v works well, h does not, to be symmetrized as mnmx, directional, hv-nonsymmetrical +g.min34v = reshape(Cooc([Uq_min Dq_min],order,'ver',T) + Cooc([Rq_min;Lq_min],order,'hor',T),[],1); +g.max34v = reshape(Cooc([Uq_max Dq_max],order,'ver',T) + Cooc([Rq_max;Lq_max],order,'hor',T),[],1); +% minmax41 -- to be symmetrized as mnmx, non-directional, hv-symmetrical +[R_min,R_max] = deal(min(RLq_min,UDq_min),max(RLq_max,UDq_max)); +g.min41 = reshape(Cooc(R_min,order,'hor',T) + Cooc(R_min,order,'ver',T),[],1); +g.max41 = reshape(Cooc(R_max,order,'hor',T) + Cooc(R_max,order,'ver',T),[],1); +% minmax34 -- good, to be symmetrized as mnmx, directional, hv-symmetrical +[RUq_min,RDq_min,LUq_min,LDq_min] = deal(min(RUq_min,RUq),min(RDq_min,RDq),min(LUq_min,LUq),min(LDq_min,LDq)); +[RUq_max,RDq_max,LUq_max,LDq_max] = deal(max(RUq_max,RUq),max(RDq_max,RDq),max(LUq_max,LUq),max(LDq_max,LDq)); +g.min34 = reshape(Cooc([RUq_min;RDq_min;LUq_min;LDq_min],order,'hor',T) + Cooc([RUq_min RDq_min LUq_min LDq_min],order,'ver',T),[],1); +g.max34 = reshape(Cooc([RUq_max;RDq_max;LUq_max;LDq_max],order,'hor',T) + Cooc([RUq_max RDq_max LUq_max LDq_max],order,'ver',T),[],1); +% minmax48h -- h better than v, to be symmetrized as mnmx, directional, hv-nonsymmetrical. 48v is almost as good as 48h; for 3rd-order but weaker for 1st-order. Here, I am outputting both but Figure 1 in our paper lists only 48h. +[RUq_min2,RDq_min2,LDq_min2,LUq_min2] = deal(min(RUq_min,LUq),min(RDq_min,RUq),min(LDq_min,RDq),min(LUq_min,LDq)); +[RUq_min3,RDq_min3,LDq_min3,LUq_min3] = deal(min(RUq_min,RDq),min(RDq_min,LDq),min(LDq_min,LUq),min(LUq_min,RUq)); +g.min48h = reshape(Cooc([RUq_min2;LDq_min2;RDq_min3;LUq_min3],order,'hor',T) + Cooc([RDq_min2 LUq_min2 RUq_min3 LDq_min3],order,'ver',T),[],1); +g.min48v = reshape(Cooc([RDq_min2;LUq_min2;RUq_min3;LDq_min3],order,'hor',T) + Cooc([RUq_min2 LDq_min2 RDq_min3 LUq_min3],order,'ver',T),[],1); +[RUq_max2,RDq_max2,LDq_max2,LUq_max2] = deal(max(RUq_max,LUq),max(RDq_max,RUq),max(LDq_max,RDq),max(LUq_max,LDq)); +[RUq_max3,RDq_max3,LDq_max3,LUq_max3] = deal(max(RUq_max,RDq),max(RDq_max,LDq),max(LDq_max,LUq),max(LUq_max,RUq)); +g.max48h = reshape(Cooc([RUq_max2;LDq_max2;RDq_max3;LUq_max3],order,'hor',T) + Cooc([RDq_max2 LUq_max2 RUq_max3 LDq_max3],order,'ver',T),[],1); +g.max48v = reshape(Cooc([RDq_max2;LUq_max2;RUq_max3;LDq_max3],order,'hor',T) + Cooc([RUq_max2 LDq_max2 RDq_max3 LUq_max3],order,'ver',T),[],1); +% minmax54 -- to be symmetrized as mnmx, directional, hv-symmetrical +[RUq_min4,RDq_min4,LDq_min4,LUq_min4] = deal(min(RUq_min2,RDq),min(RDq_min2,LDq),min(LDq_min2,LUq),min(LUq_min2,RUq)); +[RUq_min5,RDq_min5,LDq_min5,LUq_min5] = deal(min(RUq_min3,LUq),min(RDq_min3,RUq),min(LDq_min3,RDq),min(LUq_min3,LDq)); +g.min54 = reshape(Cooc([RUq_min4;LDq_min4;RDq_min5;LUq_min5],order,'hor',T) + Cooc([RDq_min4 LUq_min4 RUq_min5 LDq_min5],order,'ver',T),[],1); +[RUq_max4,RDq_max4,LDq_max4,LUq_max4] = deal(max(RUq_max2,RDq),max(RDq_max2,LDq),max(LDq_max2,LUq),max(LUq_max2,RUq)); +[RUq_max5,RDq_max5,LDq_max5,LUq_max5] = deal(max(RUq_max3,LUq),max(RDq_max3,RUq),max(LDq_max3,RDq),max(LUq_max3,LDq)); +g.max54 = reshape(Cooc([RUq_max4;LDq_max4;RDq_max5;LUq_max5],order,'hor',T) + Cooc([RDq_max4 LUq_max4 RUq_max5 LDq_max5],order,'ver',T),[],1); +function g = all2nd(X,q) +% +% X must be a matrix of doubles or singles (the image) and q is the +% quantization step (any positive number). +% +% Recommended values of q are c, 1.5c, 2c, where c is the central +% coefficient in the differential (at X(I,J)). +% +% This function outputs co-occurrences of ALL 2nd-order residuals +% listed in Figure 1 in our journal HUGO paper (version from June 14), +% including the naming convention. +% +% List of outputted features: +% +% 1a) spam12h +% 1b) spam12v (orthogonal-spam) +% 1c) minmax21 +% 1d) minmax41 +% 1e) minmax24h (24v is also outputted but not listed in Figure 1) +% 1f) minmax32 +% +% Naming convention: +% +% name = {type}{f}{sigma}{scan} +% type \in {spam, minmax} +% f \in {1,2,3,4,5} number of filters that are "minmaxed" +% sigma \in {1,2,3,4,8} symmetry index +% scan \in {h,v,\emptyset} scan of the cooc matrix (empty = sum of both +% h and v scans). +% +% All even residuals are implemented the same way simply by +% narrowing the range for I and J and replacing the residuals. +% +% Note1: The term X(I,J) should always have the "-" sign. +% Note2: This script does not include s, so, cout, cin versions (weak). +% +% This function calls Residual.m, Cooc.m, and Quant.m + +[T,order] = deal(2,4); +% 2nd-order residuals are implemented using Residual.m +[Dh,Dv,Dd,Dm] = deal(Residual(X,2,'hor'),Residual(X,2,'ver'),Residual(X,2,'diag'),Residual(X,2,'mdiag')); +[Yh,Yv,Yd,Ym] = deal(Quant(Dh,q,T),Quant(Dv,q,T),Quant(Dd,q,T),Quant(Dm,q,T)); +% spam12h/v +g.spam12h = reshape(Cooc(Yh,order,'hor',T) + Cooc(Yv,order,'ver',T),[],1); +g.spam12v = reshape(Cooc(Yh,order,'ver',T) + Cooc(Yv,order,'hor',T),[],1); +% minmax21 +[Dmin,Dmax] = deal(min(Yh,Yv),max(Yh,Yv)); +g.min21 = reshape(Cooc(Dmin,order,'hor',T) + Cooc(Dmin,order,'ver',T),[],1); +g.max21 = reshape(Cooc(Dmax,order,'hor',T) + Cooc(Dmax,order,'ver',T),[],1); +% minmax41 +[Dmin2,Dmax2] = deal(min(Dmin,min(Yd,Ym)),max(Dmax,max(Yd,Ym))); +g.min41 = reshape(Cooc(Dmin2,order,'hor',T) + Cooc(Dmin2,order,'ver',T),[],1); +g.max41 = reshape(Cooc(Dmax2,order,'hor',T) + Cooc(Dmax2,order,'ver',T),[],1); +% minmax32 -- good, directional, hv-symmetrical, to be symmetrized as mnmx +[RUq_min,RDq_min] = deal(min(Dmin,Ym),min(Dmin,Yd)); +[RUq_max,RDq_max] = deal(max(Dmax,Ym),max(Dmax,Yd)); +g.min32 = reshape(Cooc([RUq_min;RDq_min],order,'hor',T) + Cooc([RUq_min RDq_min],order,'ver',T),[],1); +g.max32 = reshape(Cooc([RUq_max;RDq_max],order,'hor',T) + Cooc([RUq_max RDq_max],order,'ver',T),[],1); +% minmax24h,v -- both "not bad," h slightly better, directional, hv-nonsymmetrical, to be symmetrized as mnmx +[RUq_min2,RDq_min2,RUq_min3,LUq_min3] = deal(min(Ym,Yh),min(Yd,Yh),min(Ym,Yv),min(Yd,Yv)); +g.min24h = reshape(Cooc([RUq_min2;RDq_min2],order,'hor',T)+Cooc([RUq_min3 LUq_min3],order,'ver',T),[],1); +g.min24v = reshape(Cooc([RUq_min2 RDq_min2],order,'ver',T)+Cooc([RUq_min3;LUq_min3],order,'hor',T),[],1); +[RUq_max2,RDq_max2,RUq_max3,LUq_max3] = deal(max(Ym,Yh),max(Yd,Yh),max(Ym,Yv),max(Yd,Yv)); +g.max24h = reshape(Cooc([RUq_max2;RDq_max2],order,'hor',T)+Cooc([RUq_max3 LUq_max3],order,'ver',T),[],1); +g.max24v = reshape(Cooc([RUq_max2 RDq_max2],order,'ver',T)+Cooc([RUq_max3;LUq_max3],order,'hor',T),[],1); +function g = all3rd(X,q) +% +% X must be a matrix of doubles or singles (the image) and q is the +% quantization step (any positive number). +% +% Recommended values of q are c, 1.5c, 2c, where c is the central +% coefficient in the differential (at X(I,J)). +% +% This function outputs co-occurrences of ALL 3rd-order residuals +% listed in Figure 1 in our journal HUGO paper (version from June 14), +% including the naming convention. +% +% List of outputted features: +% +% 1a) spam14h +% 1b) spam14v (orthogonal-spam) +% 1c) minmax22v +% 1d) minmax24 +% 1e) minmax34v +% 1f) minmax41 +% 1g) minmax34 +% 1h) minmax48h +% 1i) minmax54 +% +% Naming convention: +% +% name = {type}{f}{sigma}{scan} +% type \in {spam, minmax} +% f \in {1,2,3,4,5} number of filters that are "minmaxed" +% sigma \in {1,2,3,4,8} symmetry index +% scan \in {h,v,\emptyset} scan of the cooc matrix (empty = sum of both +% h and v scans). +% +% All odd residuals are implemented the same way simply by +% narrowing the range for I and J and replacing the residuals -- +% -- they should "stick out" (trcet) in the same direction as +% the 1st order ones. For example, for the 3rd order: +% +% RU = -X(I-2,J+2)+3*X(I-1,J+1)-3*X(I,J)+X(I+1,J-1); ... etc. +% +% Note1: The term X(I,J) should always have the "-" sign. +% Note2: This script does not include s, so, cout, cin versions (weak). + +[M N] = size(X); [I,J,T,order] = deal(3:M-2,3:N-2,2,4); +[R,L,U,D] = deal(-X(I,J+2)+3*X(I,J+1)-3*X(I,J)+X(I,J-1),-X(I,J-2)+3*X(I,J-1)-3*X(I,J)+X(I,J+1),-X(I-2,J)+3*X(I-1,J)-3*X(I,J)+X(I+1,J),-X(I+2,J)+3*X(I+1,J)-3*X(I,J)+X(I-1,J)); +[Rq,Lq,Uq,Dq] = deal(Quant(R,q,T),Quant(L,q,T),Quant(U,q,T),Quant(D,q,T)); +[RU,LU,RD,LD] = deal(-X(I-2,J+2)+3*X(I-1,J+1)-3*X(I,J)+X(I+1,J-1),-X(I-2,J-2)+3*X(I-1,J-1)-3*X(I,J)+X(I+1,J+1),-X(I+2,J+2)+3*X(I+1,J+1)-3*X(I,J)+X(I-1,J-1),-X(I+2,J-2)+3*X(I+1,J-1)-3*X(I,J)+X(I-1,J+1)); +[RUq,RDq,LUq,LDq] = deal(Quant(RU,q,T),Quant(RD,q,T),Quant(LU,q,T),Quant(LD,q,T)); +% minmax22h -- to be symmetrized as mnmx, directional, hv-nonsymmetrical +[RLq_min,UDq_min] = deal(min(Rq,Lq),min(Uq,Dq)); +[RLq_max,UDq_max] = deal(max(Rq,Lq),max(Uq,Dq)); +g.min22h = reshape(Cooc(RLq_min,order,'hor',T) + Cooc(UDq_min,order,'ver',T),[],1); +g.max22h = reshape(Cooc(RLq_max,order,'hor',T) + Cooc(UDq_max,order,'ver',T),[],1); +% minmax34h -- to be symmetrized as mnmx, directional, hv-nonsymmetrical +[Uq_min,Rq_min,Dq_min,Lq_min] = deal(min(RLq_min,Uq),min(UDq_min,Rq),min(RLq_min,Dq),min(UDq_min,Lq)); +[Uq_max,Rq_max,Dq_max,Lq_max] = deal(max(RLq_max,Uq),max(UDq_max,Rq),max(RLq_max,Dq),max(UDq_max,Lq)); +g.min34h = reshape(Cooc([Uq_min;Dq_min],order,'hor',T)+Cooc([Rq_min Lq_min],order,'ver',T),[],1); +g.max34h = reshape(Cooc([Uq_max;Dq_max],order,'hor',T)+Cooc([Rq_max Lq_max],order,'ver',T),[],1); +% spam14h,v -- to be symmetrized as spam, directional, hv-nonsymmetrical +g.spam14h = reshape(Cooc(Rq,order,'hor',T) + Cooc(Uq,order,'ver',T),[],1); +g.spam14v = reshape(Cooc(Rq,order,'ver',T) + Cooc(Uq,order,'hor',T),[],1); +% minmax22v -- to be symmetrized as mnmx, directional, hv-nonsymmetrical. Good with higher-order residuals! Note: 22h is bad (too much neighborhood overlap). +g.min22v = reshape(Cooc(RLq_min,order,'ver',T) + Cooc(UDq_min,order,'hor',T),[],1); +g.max22v = reshape(Cooc(RLq_max,order,'ver',T) + Cooc(UDq_max,order,'hor',T),[],1); +% minmax24 -- to be symmetrized as mnmx, directional, hv-symmetrical Note: Darn good, too. +[RUq_min,RDq_min,LUq_min,LDq_min] = deal(min(Rq,Uq),min(Rq,Dq),min(Lq,Uq),min(Lq,Dq)); +[RUq_max,RDq_max,LUq_max,LDq_max] = deal(max(Rq,Uq),max(Rq,Dq),max(Lq,Uq),max(Lq,Dq)); +g.min24 = reshape(Cooc([RUq_min;RDq_min;LUq_min;LDq_min],order,'hor',T) + Cooc([RUq_min RDq_min LUq_min LDq_min],order,'ver',T),[],1); +g.max24 = reshape(Cooc([RUq_max;RDq_max;LUq_max;LDq_max],order,'hor',T) + Cooc([RUq_max RDq_max LUq_max LDq_max],order,'ver',T),[],1); +% minmax34v -- v works well, h does not, to be symmetrized as mnmx, directional, hv-nonsymmetrical +g.min34v = reshape(Cooc([Uq_min Dq_min],order,'ver',T) + Cooc([Rq_min;Lq_min],order,'hor',T),[],1); +g.max34v = reshape(Cooc([Uq_max Dq_max],order,'ver',T) + Cooc([Rq_max;Lq_max],order,'hor',T),[],1); +% minmax41 -- unknown performance as of 6/14/11, to be symmetrized as mnmx, non-directional, hv-symmetrical +[R_min,R_max] = deal(min(RUq_min,LDq_min),max(RUq_max,LDq_max)); +g.min41 = reshape(Cooc(R_min,order,'hor',T) + Cooc(R_min,order,'ver',T),[],1); +g.max41 = reshape(Cooc(R_max,order,'hor',T) + Cooc(R_max,order,'ver',T),[],1); +% minmax34 -- good, to be symmetrized as mnmx, directional, hv-symmetrical +[RUq_min2,RDq_min2,LUq_min2,LDq_min2] = deal(min(RUq_min,RUq),min(RDq_min,RDq),min(LUq_min,LUq),min(LDq_min,LDq)); +[RUq_max2,RDq_max2,LUq_max2,LDq_max2] = deal(max(RUq_max,RUq),max(RDq_max,RDq),max(LUq_max,LUq),max(LDq_max,LDq)); +g.min34 = reshape(Cooc([RUq_min2;RDq_min2;LUq_min2;LDq_min2],order,'hor',T) + Cooc([RUq_min2 RDq_min2 LUq_min2 LDq_min2],order,'ver',T),[],1); +g.max34 = reshape(Cooc([RUq_max2;RDq_max2;LUq_max2;LDq_max2],order,'hor',T) + Cooc([RUq_max2 RDq_max2 LUq_max2 LDq_max2],order,'ver',T),[],1); +% minmax48h -- h better than v, to be symmetrized as mnmx, directional, hv-nonsymmetrical. 48v is almost as good as 48h for 3rd-order but weaker for 1st-order. Here, I am outputting both but Figure 1 in our paper lists only 48h. +[RUq_min3,RDq_min3,LDq_min3,LUq_min3] = deal(min(RUq_min2,LUq),min(RDq_min2,RUq),min(LDq_min2,RDq),min(LUq_min2,LDq)); +[RUq_min4,RDq_min4,LDq_min4,LUq_min4] = deal(min(RUq_min2,RDq),min(RDq_min2,LDq),min(LDq_min2,LUq),min(LUq_min2,RUq)); +g.min48h = reshape(Cooc([RUq_min3;LDq_min3;RDq_min4;LUq_min4],order,'hor',T)+Cooc([RDq_min3 LUq_min3 RUq_min4 LDq_min4],order,'ver',T),[],1); +g.min48v = reshape(Cooc([RUq_min3 LDq_min3 RDq_min4 LUq_min4],order,'ver',T)+Cooc([RDq_min3;LUq_min3;RUq_min4;LDq_min4],order,'hor',T),[],1); +[RUq_max3,RDq_max3,LDq_max3,LUq_max3] = deal(max(RUq_max2,LUq),max(RDq_max2,RUq),max(LDq_max2,RDq),max(LUq_max2,LDq)); +[RUq_max4,RDq_max4,LDq_max4,LUq_max4] = deal(max(RUq_max2,RDq),max(RDq_max2,LDq),max(LDq_max2,LUq),max(LUq_max2,RUq)); +g.max48h = reshape(Cooc([RUq_max3;LDq_max3;RDq_max4;LUq_max4],order,'hor',T)+Cooc([RDq_max3 LUq_max3 RUq_max4 LDq_max4],order,'ver',T),[],1); +g.max48v = reshape(Cooc([RUq_max3 LDq_max3 RDq_max4 LUq_max4],order,'ver',T)+Cooc([RDq_max3;LUq_max3;RUq_max4;LDq_max4],order,'hor',T),[],1); +% minmax54 -- to be symmetrized as mnmx, directional, hv-symmetrical +[RUq_min5,RDq_min5,LDq_min5,LUq_min5] = deal(min(RUq_min3,RDq),min(RDq_min3,LDq),min(LDq_min3,LUq),min(LUq_min3,RUq)); +[RUq_max5,RDq_max5,LDq_max5,LUq_max5] = deal(max(RUq_max3,RDq),max(RDq_max3,LDq),max(LDq_max3,LUq),max(LUq_max3,RUq)); +g.min54 = reshape(Cooc([RUq_min5;LDq_min5;RDq_min5;LUq_min5],order,'hor',T) + Cooc([RDq_min5 LUq_min5 RUq_min5 LDq_min5],order,'ver',T),[],1); +g.max54 = reshape(Cooc([RUq_max5;LDq_max5;RDq_max5;LUq_max5],order,'hor',T) + Cooc([RDq_max5 LUq_max5 RUq_max5 LDq_max5],order,'ver',T),[],1); +function g = all3x3(X,q) +% This function outputs co-occurrences of ALL residuals based on the +% KB kernel and its "halves" (EDGE residuals) as listed in Figure 1 +% in our journal HUGO paper (version from June 14), including the naming +% convention. +[T,order] = deal(2,4); +% spam11 (old name KB residual), good, non-directional, hv-symmetrical, to be symmetrized as spam +D = Residual(X,2,'KB'); Y = Quant(D,q,T); +g.spam11 = reshape(Cooc(Y,order,'hor',T) + Cooc(Y,order,'ver',T),[],1); +% EDGE residuals +D = Residual(X,2,'edge-h');Du = D(:,1:size(D,2)/2);Db = D(:,size(D,2)/2+1:end); +D = Residual(X,2,'edge-v');Dl = D(:,1:size(D,2)/2);Dr = D(:,size(D,2)/2+1:end); +[Yu,Yb,Yl,Yr] = deal(Quant(Du,q,T),Quant(Db,q,T),Quant(Dl,q,T),Quant(Dr,q,T)); +% spam14h,v not bad, directional, hv-nonsym, to be symmetrized as spam +g.spam14v = reshape(Cooc([Yu Yb],order,'ver',T) + Cooc([Yl;Yr],order,'hor',T),[],1); +g.spam14h = reshape(Cooc([Yu;Yb],order,'hor',T) + Cooc([Yl Yr],order,'ver',T),[],1); +% minmax24 -- EXCELLENT, directional, hv-sym, to be symmetrized as mnmx +[Dmin1,Dmin2,Dmin3,Dmin4] = deal(min(Yu,Yl),min(Yb,Yr),min(Yu,Yr),min(Yb,Yl)); +g.min24 = reshape(Cooc([Dmin1 Dmin2 Dmin3 Dmin4],order,'ver',T) + Cooc([Dmin1;Dmin2;Dmin3;Dmin4],order,'hor',T),[],1); +[Dmax1,Dmax2,Dmax3,Dmax4] = deal(max(Yu,Yl),max(Yb,Yr),max(Yu,Yr),max(Yb,Yl)); +g.max24 = reshape(Cooc([Dmax1 Dmax2 Dmax3 Dmax4],order,'ver',T) + Cooc([Dmax1;Dmax2;Dmax3;Dmax4],order,'hor',T),[],1); +% minmax22 - hv-nonsymmetrical +% min22h -- good, to be symmetrized as mnmx, directional, hv-nonsymmetrical +% min22v -- EXCELLENT - to be symmetrized as mnmx, directional, +[UEq_min,REq_min] = deal(min(Yu,Yb),min(Yr,Yl)); +g.min22h = reshape(Cooc(UEq_min,order,'hor',T) + Cooc(REq_min,order,'ver',T),[],1); +g.min22v = reshape(Cooc(UEq_min,order,'ver',T) + Cooc(REq_min,order,'hor',T),[],1); +[UEq_max,REq_max] = deal(max(Yu,Yb),max(Yr,Yl)); +g.max22h = reshape(Cooc(UEq_max,order,'hor',T) + Cooc(REq_max,order,'ver',T),[],1); +g.max22v = reshape(Cooc(UEq_max,order,'ver',T) + Cooc(REq_max,order,'hor',T),[],1); +% minmax41 -- good, non-directional, hv-sym, to be symmetrized as mnmx +[Dmin5,Dmax5] = deal(min(Dmin1,Dmin2),max(Dmax1,Dmax2)); +g.min41 = reshape(Cooc(Dmin5,order,'ver',T) + Cooc(Dmin5,order,'hor',T),[],1); +g.max41 = reshape(Cooc(Dmax5,order,'ver',T) + Cooc(Dmax5,order,'hor',T),[],1); +function g = all5x5(X,q) +% This function outputs co-occurrences of ALL residuals based on the +% KV kernel and its "halves" (EDGE residuals) as listed in Figure 1 +% in our journal HUGO paper (version from June 14), including the naming +% convention. +[M N] = size(X); [I,J,T,order] = deal(3:M-2,3:N-2,2,4); +% spam11 (old name KV residual), good, non-directional, hv-symmetrical, to be symmetrized as spam +D = Residual(X,3,'KV'); Y = Quant(D,q,T); +g.spam11 = reshape(Cooc(Y,order,'hor',T) + Cooc(Y,order,'ver',T),[],1); +% EDGE residuals +Du = 8*X(I,J-1)+8*X(I-1,J)+8*X(I,J+1)-6*X(I-1,J-1)-6*X(I-1,J+1)-2*X(I,J-2)-2*X(I,J+2)-2*X(I-2,J)+2*X(I-1,J-2)+2*X(I-2,J-1)+2*X(I-2,J+1)+2*X(I-1,J+2)-X(I-2,J-2)-X(I-2,J+2)-12*X(I,J); +Dr = 8*X(I-1,J)+8*X(I,J+1)+8*X(I+1,J)-6*X(I-1,J+1)-6*X(I+1,J+1)-2*X(I-2,J)-2*X(I+2,J)-2*X(I,J+2)+2*X(I-2,J+1)+2*X(I-1,J+2)+2*X(I+1,J+2)+2*X(I+2,J+1)-X(I-2,J+2)-X(I+2,J+2)-12*X(I,J); +Db = 8*X(I,J+1)+8*X(I+1,J)+8*X(I,J-1)-6*X(I+1,J+1)-6*X(I+1,J-1)-2*X(I,J-2)-2*X(I,J+2)-2*X(I+2,J)+2*X(I+1,J+2)+2*X(I+2,J+1)+2*X(I+2,J-1)+2*X(I+1,J-2)-X(I+2,J+2)-X(I+2,J-2)-12*X(I,J); +Dl = 8*X(I+1,J)+8*X(I,J-1)+8*X(I-1,J)-6*X(I+1,J-1)-6*X(I-1,J-1)-2*X(I-2,J)-2*X(I+2,J)-2*X(I,J-2)+2*X(I+2,J-1)+2*X(I+1,J-2)+2*X(I-1,J-2)+2*X(I-2,J-1)-X(I+2,J-2)-X(I-2,J-2)-12*X(I,J); +[Yu,Yb,Yl,Yr] = deal(Quant(Du,q,T),Quant(Db,q,T),Quant(Dl,q,T),Quant(Dr,q,T)); +% spam14v not bad, directional, hv-nonsym, to be symmetrized as spam +g.spam14v = reshape(Cooc([Yu Yb],order,'ver',T) + Cooc([Yl;Yr],order,'hor',T),[],1); +g.spam14h = reshape(Cooc([Yu;Yb],order,'hor',T) + Cooc([Yl Yr],order,'ver',T),[],1); +% minmax24 -- EXCELLENT, directional, hv-sym, to be symmetrized as mnmx +[Dmin1,Dmin2,Dmin3,Dmin4] = deal(min(Yu,Yl),min(Yb,Yr),min(Yu,Yr),min(Yb,Yl)); +g.min24 = reshape(Cooc([Dmin1 Dmin2 Dmin3 Dmin4],order,'ver',T) + Cooc([Dmin1;Dmin2;Dmin3;Dmin4],order,'hor',T),[],1); +[Dmax1,Dmax2,Dmax3,Dmax4] = deal(max(Yu,Yl),max(Yb,Yr),max(Yu,Yr),max(Yb,Yl)); +g.max24 = reshape(Cooc([Dmax1 Dmax2 Dmax3 Dmax4],order,'ver',T) + Cooc([Dmax1;Dmax2;Dmax3;Dmax4],order,'hor',T),[],1); +% minmax22 - hv-nonsymmetrical +% min22h -- good, to be symmetrized as mnmx, directional, hv-nonsymmetrical +% min22v -- EXCELLENT - to be symmetrized as mnmx, directional, +[UEq_min,REq_min] = deal(min(Yu,Yb),min(Yr,Yl)); +g.min22h = reshape(Cooc(UEq_min,order,'hor',T) + Cooc(REq_min,order,'ver',T),[],1); +g.min22v = reshape(Cooc(UEq_min,order,'ver',T) + Cooc(REq_min,order,'hor',T),[],1); +[UEq_max,REq_max] = deal(max(Yu,Yb),max(Yr,Yl)); +g.max22h = reshape(Cooc(UEq_max,order,'hor',T) + Cooc(REq_max,order,'ver',T),[],1); +g.max22v = reshape(Cooc(UEq_max,order,'ver',T) + Cooc(REq_max,order,'hor',T),[],1); +% minmax41 -- good, non-directional, hv-sym, to be symmetrized as mnmx +[Dmin5,Dmax5] = deal(min(Dmin1,Dmin2),max(Dmax1,Dmax2)); +g.min41 = reshape(Cooc(Dmin5,order,'ver',T) + Cooc(Dmin5,order,'hor',T),[],1); +g.max41 = reshape(Cooc(Dmax5,order,'ver',T) + Cooc(Dmax5,order,'hor',T),[],1); +function f = Cooc(D,order,type,T) +% Co-occurrence operator to be appied to a 2D array of residuals D \in [-T,T] +% T ... threshold +% order ... cooc order \in {1,2,3,4,5} +% type ... cooc type \in {hor,ver,diag,mdiag,square,square-ori,hvdm} +% f ... an array of size (2T+1)^order + +B = 2*T+1; +if max(abs(D(:))) > T, fprintf('*** ERROR in Cooc.m: Residual out of range ***\n'), end + +switch order + case 1 + f = hist(D(:),-T:T); + case 2 + f = zeros(B,B); + if strcmp(type,'hor'), L = D(:,1:end-1); R = D(:,2:end);end + if strcmp(type,'ver'), L = D(1:end-1,:); R = D(2:end,:);end + if strcmp(type,'diag'), L = D(1:end-1,1:end-1); R = D(2:end,2:end);end + if strcmp(type,'mdiag'), L = D(1:end-1,2:end); R = D(2:end,1:end-1);end + for i = -T : T + R2 = R(L(:)==i); + for j = -T : T + f(i+T+1,j+T+1) = sum(R2(:)==j); + end + end + case 3 + f = zeros(B,B,B); + if strcmp(type,'hor'), L = D(:,1:end-2); C = D(:,2:end-1); R = D(:,3:end);end + if strcmp(type,'ver'), L = D(1:end-2,:); C = D(2:end-1,:); R = D(3:end,:);end + if strcmp(type,'diag'), L = D(1:end-2,1:end-2); C = D(2:end-1,2:end-1); R = D(3:end,3:end);end + if strcmp(type,'mdiag'), L = D(1:end-2,3:end); C = D(2:end-1,2:end-1); R = D(3:end,1:end-2);end + for i = -T : T + C2 = C(L(:)==i); + R2 = R(L(:)==i); + for j = -T : T + R3 = R2(C2(:)==j); + for k = -T : T + f(i+T+1,j+T+1,k+T+1) = sum(R3(:)==k); + end + end + end + case 4 + f = zeros(B,B,B,B); + if strcmp(type,'hor'), L = D(:,1:end-3); C = D(:,2:end-2); E = D(:,3:end-1); R = D(:,4:end);end + if strcmp(type,'ver'), L = D(1:end-3,:); C = D(2:end-2,:); E = D(3:end-1,:); R = D(4:end,:);end + if strcmp(type,'diag'), L = D(1:end-3,1:end-3); C = D(2:end-2,2:end-2); E = D(3:end-1,3:end-1); R = D(4:end,4:end);end + if strcmp(type,'mdiag'), L = D(4:end,1:end-3); C = D(3:end-1,2:end-2); E = D(2:end-2,3:end-1); R = D(1:end-3,4:end);end + if strcmp(type,'square'), L = D(2:end,1:end-1); C = D(2:end,2:end); E = D(1:end-1,2:end); R = D(1:end-1,1:end-1);end + if strcmp(type,'square-ori'), [M, N] = size(D); Dh = D(:,1:M); Dv = D(:,M+1:2*M); + L = Dh(2:end,1:end-1); C = Dv(2:end,2:end); E = Dh(1:end-1,2:end); R = Dv(1:end-1,1:end-1);end + if strcmp(type,'hvdm'), [M, N] = size(D); L = D(:,1:M); C = D(:,M+1:2*M); E = D(:,2*M+1:3*M); R = D(:,3*M+1:4*M);end + if strcmp(type,'s-in'), [M, N] = size(D); Dh = D(:,1:M); Dv = D(:,M+1:2*M); Dh1 = D(:,2*M+1:3*M); Dv1 = D(:,3*M+1:4*M); + L = Dh(2:end,1:end-1); C = Dh1(2:end,2:end); E = Dh1(1:end-1,2:end); R = Dh(1:end-1,1:end-1);end + if strcmp(type,'s-out'), [M, N] = size(D); Dh = D(:,1:M); Dv = D(:,M+1:2*M); Dh1 = D(:,2*M+1:3*M); Dv1 = D(:,3*M+1:4*M); + L = Dh1(2:end,1:end-1); C = Dh(2:end,2:end); E = Dh(1:end-1,2:end); R = Dh1(1:end-1,1:end-1);end + if strcmp(type,'ori-in'), [M, N] = size(D); Dh = D(:,1:M); Dv = D(:,M+1:2*M); Dh1 = D(:,2*M+1:3*M); Dv1 = D(:,3*M+1:4*M); + L = Dh(2:end,1:end-1); C = Dv1(2:end,2:end); E = Dh1(1:end-1,2:end); R = Dv(1:end-1,1:end-1);end + if strcmp(type,'ori-out'),[M, N] = size(D); Dh = D(:,1:M); Dv = D(:,M+1:2*M); Dh1 = D(:,2*M+1:3*M); Dv1 = D(:,3*M+1:4*M); + L = Dh1(2:end,1:end-1); C = Dv(2:end,2:end); E = Dh(1:end-1,2:end); R = Dv1(1:end-1,1:end-1);end + for i = -T : T + ind = (L(:)==i); C2 = C(ind); E2 = E(ind); R2 = R(ind); + for j = -T : T + ind = (C2(:)==j); E3 = E2(ind); R3 = R2(ind); + for k = -T : T + R4 = R3(E3(:)==k); + for l = -T : T + f(i+T+1,j+T+1,k+T+1,l+T+1) = sum(R4(:)==l); + end + end + end + end + case 5 + f = zeros(B,B,B,B,B); + if strcmp(type,'hor'),L = D(:,1:end-4); C = D(:,2:end-3); E = D(:,3:end-2); F = D(:,4:end-1); R = D(:,5:end);end + if strcmp(type,'ver'),L = D(1:end-4,:); C = D(2:end-3,:); E = D(3:end-2,:); F = D(4:end-1,:); R = D(5:end,:);end + + for i = -T : T + ind = (L(:)==i); C2 = C(ind); E2 = E(ind); F2 = F(ind); R2 = R(ind); + for j = -T : T + ind = (C2(:)==j); E3 = E2(ind); F3 = F2(ind); R3 = R2(ind); + for k = -T : T + ind = (E3(:)==k); F4 = F3(ind); R4 = R3(ind); + for l = -T : T + R5 = R4(F4(:)==l); + for m = -T : T + f(i+T+1,j+T+1,k+T+1,l+T+1,m+T+1) = sum(R5(:)==m); + end + end + end + end + end +end + +% normalization +f = double(f); +fsum = sum(f(:)); +if fsum>0, f = f/fsum; end + +function Y = Quant(X,q,T) +% Quantization routine +% X ... variable to be quantized/truncated +% T ... threshold +% q ... quantization step (with type = 'scalar') or a vector of increasing +% non-negative integers outlining the quantization process. +% Y ... quantized/truncated variable +% Example 0: when q is a positive scalar, Y = trunc(round(X/q),T) +% Example 1: q = [0 1 2 3] quantizes 0 to 0, 1 to 1, 2 to 2, [3,Inf) to 3, +% (-Inf,-3] to -3, -2 to -2, -1 to -1. It is equivalent to Quant(.,3,1). +% Example 2: q = [0 2 4 5] quantizes 0 to 0, {1,2} to 1, {3,4} to 2, +% [5,Inf) to 3, and similarly with the negatives. +% Example 3: q = [1 2] quantizes {-1,0,1} to 0, [2,Inf) to 1, (-Inf,-2] to -1. +% Example 4: q = [1 3 7 15 16] quantizes {-1,0,1} to 0, {2,3} to 1, {4,5,6,7} +% to 2, {8,9,10,11,12,13,14,15} to 3, [16,Inf) to 4, and similarly the +% negatives. + +if numel(q) == 1 + if q > 0, Y = trunc(round(X/q),T); + else fprintf('*** ERROR: Attempt to quantize with non-positive step. ***\n'),end +else + q = round(q); % Making sure the vector q is made of integers + if min(q(2:end)-q(1:end-1)) <= 0 + fprintf('*** ERROR: quantization vector not strictly increasing. ***\n') + end + if min(q) < 0, fprintf('*** ERROR: Attempt to quantize with negative step. ***\n'),end + + T = q(end); % The last value determines the truncation threshold + v = zeros(1,2*T+1); % value-substitution vector + Y = trunc(X,T)+T+1; % Truncated X and shifted to positive values + if q(1) == 0 + v(T+1) = 0; z = 1; ind = T+2; + for i = 2 : numel(q) + v(ind:ind+q(i)-q(i-1)-1) = z; + ind = ind+q(i)-q(i-1); + z = z+1; + end + v(1:T) = -v(end:-1:T+2); + else + v(T+1-q(1):T+1+q(1)) = 0; z = 1; ind = T+2+q(1); + for i = 2 : numel(q) + v(ind:ind+q(i)-q(i-1)-1) = z; + ind = ind+q(i)-q(i-1); + z = z+1; + end + v(1:T-q(1)) = -v(end:-1:T+2+q(1)); + end + Y = v(Y); % The actual quantization :) +end +function Z = trunc(X,T) +% Truncation to [-T,T] +Z = X; +Z(Z > T) = T; +Z(Z < -T) = -T; +function fsym = symfea(f,T,order,type) +% Marginalization by sign and directional symmetry for a feature vector +% stored as one of our 2*(2T+1)^order-dimensional feature vectors. This +% routine should be used for features possiessing sign and directional +% symmetry, such as spam-like features or 3x3 features. It should NOT be +% used for features from MINMAX residuals. Use the alternative +% symfea_minmax for this purpose. +% The feature f is assumed to be a 2dim x database_size matrix of features +% stored as columns (e.g., hor+ver, diag+minor_diag), with dim = +% 2(2T+1)^order. + +[dim,N] = size(f); +B = 2*T+1; +c = B^order; +ERR = 1; + +if strcmp(type,'spam') + if dim == 2*c + switch order % Reduced dimensionality for a B^order dimensional feature vector + case 1, red = T + 1; + case 2, red = (T + 1)^2; + case 3, red = 1 + 3*T + 4*T^2 + 2*T^3; + case 4, red = B^2 + 4*T^2*(T + 1)^2; + case 5, red = 1/4*(B^2 + 1)*(B^3 + 1); + end + fsym = zeros(2*red,N); + + for i = 1 : N + switch order + case 1, cube = f(1:c,i); + case 2, cube = reshape(f(1:c,i),[B B]); + case 3, cube = reshape(f(1:c,i),[B B B]); + case 4, cube = reshape(f(1:c,i),[B B B B]); + case 5, cube = reshape(f(1:c,i),[B B B B B]); + end + % [size(symm_dir(cube,T,order)) red] + fsym(1:red,i) = symm(cube,T,order); + switch order + case 1, cube = f(c+1:2*c,i); + case 2, cube = reshape(f(c+1:2*c,i),[B B]); + case 3, cube = reshape(f(c+1:2*c,i),[B B B]); + case 4, cube = reshape(f(c+1:2*c,i),[B B B B]); + case 5, cube = reshape(f(c+1:2*c,i),[B B B B B]); + end + fsym(red+1:2*red,i) = symm(cube,T,order); + end + else + fsym = []; + fprintf('*** ERROR: feature dimension is not 2x(2T+1)^order. ***\n') + end + ERR = 0; +end + +if strcmp(type,'mnmx') + if dim == 2*c + switch order + case 3, red = B^3 - T*B^2; % Dim of the marginalized set is (2T+1)^3-T*(2T+1)^2 + case 4, red = B^4 - 2*T*(T+1)*B^2; % Dim of the marginalized set is (2T+1)^4-2T*(T+1)*(2T+1)^2 + end + fsym = zeros(red, N); + for i = 1 : N + switch order + case 1, cube_min = f(1:c,i); cube_max = f(c+1:2*c,i); + case 2, cube_min = reshape(f(1:c,i),[B B]); cube_max = reshape(f(c+1:2*c,i),[B B]); f_signsym = cube_min + cube_max(end:-1:1,end:-1:1); + case 3, cube_min = reshape(f(1:c,i),[B B B]); cube_max = reshape(f(c+1:2*c,i),[B B B]); f_signsym = cube_min + cube_max(end:-1:1,end:-1:1,end:-1:1); + case 4, cube_min = reshape(f(1:c,i),[B B B B]); cube_max = reshape(f(c+1:2*c,i),[B B B B]); f_signsym = cube_min + cube_max(end:-1:1,end:-1:1,end:-1:1,end:-1:1); + case 5, cube_min = reshape(f(1:c,i),[B B B B B]); cube_max = reshape(f(c+1:2*c,i),[B B B B B]); f_signsym = cube_min + cube_max(end:-1:1,end:-1:1,end:-1:1,end:-1:1,end:-1:1); + end + % f_signsym = cube_min + cube_max(end:-1:1,end:-1:1,end:-1:1); + fsym(:,i) = symm_dir(f_signsym,T,order); + end + end + ERR = 0; +end + +if ERR == 1, fprintf('*** ERROR: Feature dimension and T, order incompatible. ***\n'), end +function As = symm_dir(A,T,order) +% Symmetry marginalization routine. The purpose is to reduce the feature +% dimensionality and make the features more populated. +% A is an array of features of size (2*T+1)^order, otherwise error is outputted. +% +% Directional marginalization pertains to the fact that the +% differences d1, d2, d3, ... in a natural (both cover and stego) image +% are as likely to occur as ..., d3, d2, d1. + +% Basically, we merge all pairs of bins (i,j,k, ...) and (..., k,j,i) +% as long as they are two different bins. Thus, instead of dim = +% (2T+1)^order, we decrease the dim by 1/2*{# of non-symmetric bins}. +% For order = 3, the reduced dim is (2T+1)^order - T(2T+1)^(order-1), +% for order = 4, it is (2T+1)^4 - 2T(T+1)(2T+1)^2. + +B = 2*T+1; +done = zeros(size(A)); +switch order + case 3, red = B^3 - T*B^2; % Dim of the marginalized set is (2T+1)^3-T*(2T+1)^2 + case 4, red = B^4 - 2*T*(T+1)*B^2; % Dim of the marginalized set is (2T+1)^4-2T*(T+1)*(2T+1)^2 + case 5, red = B^5 - 2*T*(T+1)*B^3; +end +As = zeros(red, 1); +m = 1; + +switch order + case 3 + for i = -T : T + for j = -T : T + for k = -T : T + if k ~= i % Asymmetric bin + if done(i+T+1,j+T+1,k+T+1) == 0 + As(m) = A(i+T+1,j+T+1,k+T+1) + A(k+T+1,j+T+1,i+T+1); % Two mirror-bins are merged + done(i+T+1,j+T+1,k+T+1) = 1; + done(k+T+1,j+T+1,i+T+1) = 1; + m = m + 1; + end + else % Symmetric bin is just copied + As(m) = A(i+T+1,j+T+1,k+T+1); + done(i+T+1,j+T+1,k+T+1) = 1; + m = m + 1; + end + end + end + end + case 4 + for i = -T : T + for j = -T : T + for k = -T : T + for n = -T : T + if (i ~= n) || (j ~= k) % Asymmetric bin + if done(i+T+1,j+T+1,k+T+1,n+T+1) == 0 + As(m) = A(i+T+1,j+T+1,k+T+1,n+T+1) + A(n+T+1,k+T+1,j+T+1,i+T+1); % Two mirror-bins are merged + done(i+T+1,j+T+1,k+T+1,n+T+1) = 1; + done(n+T+1,k+T+1,j+T+1,i+T+1) = 1; + m = m + 1; + end + else % Symmetric bin is just copied + As(m) = A(i+T+1,j+T+1,k+T+1,n+T+1); + done(i+T+1,j+T+1,k+T+1,n+T+1) = 1; + m = m + 1; + end + end + end + end + end + case 5 + for i = -T : T + for j = -T : T + for k = -T : T + for l = -T : T + for n = -T : T + if (i ~= n) || (j ~= l) % Asymmetric bin + if done(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) == 0 + As(m) = A(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) + A(n+T+1,l+T+1,k+T+1,j+T+1,i+T+1); % Two mirror-bins are merged + done(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) = 1; + done(n+T+1,l+T+1,k+T+1,j+T+1,i+T+1) = 1; + m = m + 1; + end + else % Symmetric bin is just copied + As(m) = A(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1); + done(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) = 1; + m = m + 1; + end + end + end + end + end + end + otherwise + fprintf('*** ERROR: Order not equal to 3 or 4 or 5! ***\n') +end +function fsym = symm1(f,T,order) +% Marginalization by sign and directional symmetry for a feature vector +% stored as a (2T+1)^order-dimensional array. The input feature f is +% assumed to be a dim x database_size matrix of features stored as columns. + +[dim,N] = size(f); +B = 2*T+1; +c = B^order; +ERR = 1; + +if dim == c + ERR = 0; + switch order % Reduced dimensionality for a c-dimensional feature vector + case 1, red = T + 1; + case 2, red = (T + 1)^2; + case 3, red = 1 + 3*T + 4*T^2 + 2*T^3; + case 4, red = B^2 + 4*T^2*(T + 1)^2; + case 5, red = 1/4*(B^2 + 1)*(B^3 + 1); + end + fsym = zeros(red,N); + + for i = 1 : N + switch order + case 1, cube = f(:,i); + case 2, cube = reshape(f(:,i),[B B]); + case 3, cube = reshape(f(:,i),[B B B]); + case 4, cube = reshape(f(:,i),[B B B B]); + case 5, cube = reshape(f(:,i),[B B B B B]); + end + % [size(symm_dir(cube,T,order)) red] + fsym(:,i) = symm(cube,T,order); + end +end + +if ERR == 1, fprintf('*** ERROR in symm1: Feature dimension and T, order incompatible. ***\n'), end +function As = symm(A,T,order) +% Symmetry marginalization routine. The purpose is to reduce the feature +% dimensionality and make the features more populated. It can be applied to +% 1D -- 5D co-occurrence matrices (order \in {1,2,3,4,5}) with sign and +% directional symmetries (explained below). +% A must be an array of (2*T+1)^order, otherwise error is outputted. +% +% Marginalization by symmetry pertains to the fact that, fundamentally, +% the differences between consecutive pixels in a natural image (both cover +% and stego) d1, d2, d3, ..., have the same probability of occurrence as the +% triple -d1, -d2, -d3, ... +% +% Directional marginalization pertains to the fact that the +% differences d1, d2, d3, ... in a natural (cover and stego) image are as +% likely to occur as ..., d3, d2, d1. + +ERR = 1; % Flag denoting when size of A is incompatible with the input parameters T and order +m = 2; +B = 2*T + 1; + +switch order + case 1 % First-order coocs are only symmetrized + if numel(A) == 2*T+1 + As(1) = A(T+1); % The only non-marginalized bin is the origin 0 + As(2:T+1) = A(1:T) + A(T+2:end); + As = As(:); + ERR = 0; + end + case 2 + if numel(A) == (2*T+1)^2 + As = zeros((T+1)^2, 1); + As(1) = A(T+1,T+1); % The only non-marginalized bin is the origin (0,0) + for i = -T : T + for j = -T : T + if (done(i+T+1,j+T+1) == 0) && (abs(i)+abs(j) ~= 0) + As(m) = A(i+T+1,j+T+1) + A(T+1-i,T+1-j); + done(i+T+1,j+T+1) = 1; + done(T+1-i,T+1-j) = 1; + if (i ~= j) && (done(j+T+1,i+T+1) == 0) + As(m) = As(m) + A(j+T+1,i+T+1) + A(T+1-j,T+1-i); + done(j+T+1,i+T+1) = 1; + done(T+1-j,T+1-i) = 1; + end + m = m + 1; + end + end + end + ERR = 0; + end + case 3 + if numel(A) == B^3 + done = zeros(size(A)); + As = zeros(1+3*T+4*T^2+2*T^3, 1); + As(1) = A(T+1,T+1,T+1); % The only non-marginalized bin is the origin (0,0,0) + for i = -T : T + for j = -T : T + for k = -T : T + if (done(i+T+1,j+T+1,k+T+1) == 0) && (abs(i)+abs(j)+abs(k) ~= 0) + As(m) = A(i+T+1,j+T+1,k+T+1) + A(T+1-i,T+1-j,T+1-k); + done(i+T+1,j+T+1,k+T+1) = 1; + done(T+1-i,T+1-j,T+1-k) = 1; + if (i ~= k) && (done(k+T+1,j+T+1,i+T+1) == 0) + As(m) = As(m) + A(k+T+1,j+T+1,i+T+1) + A(T+1-k,T+1-j,T+1-i); + done(k+T+1,j+T+1,i+T+1) = 1; + done(T+1-k,T+1-j,T+1-i) = 1; + end + m = m + 1; + end + end + end + end + ERR = 0; + end + case 4 + if numel(A) == (2*T+1)^4 + done = zeros(size(A)); + As = zeros(B^2 + 4*T^2*(T+1)^2, 1); + As(1) = A(T+1,T+1,T+1,T+1); % The only non-marginalized bin is the origin (0,0,0,0) + for i = -T : T + for j = -T : T + for k = -T : T + for n = -T : T + if (done(i+T+1,j+T+1,k+T+1,n+T+1) == 0) && (abs(i)+abs(j)+abs(k)+abs(n)~=0) + As(m) = A(i+T+1,j+T+1,k+T+1,n+T+1) + A(T+1-i,T+1-j,T+1-k,T+1-n); + done(i+T+1,j+T+1,k+T+1,n+T+1) = 1; + done(T+1-i,T+1-j,T+1-k,T+1-n) = 1; + if ((i ~= n) || (j ~= k)) && (done(n+T+1,k+T+1,j+T+1,i+T+1) == 0) + As(m) = As(m) + A(n+T+1,k+T+1,j+T+1,i+T+1) + A(T+1-n,T+1-k,T+1-j,T+1-i); + done(n+T+1,k+T+1,j+T+1,i+T+1) = 1; + done(T+1-n,T+1-k,T+1-j,T+1-i) = 1; + end + m = m + 1; + end + end + end + end + end + ERR = 0; + end + case 5 + if numel(A) == (2*T+1)^5 + done = zeros(size(A)); + As = zeros(1/4*(B^2 + 1)*(B^3 + 1), 1); + As(1) = A(T+1,T+1,T+1,T+1,T+1); % The only non-marginalized bin is the origin (0,0,0,0,0) + for i = -T : T + for j = -T : T + for k = -T : T + for l = -T : T + for n = -T : T + if (done(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) == 0) && (abs(i)+abs(j)+abs(k)+abs(l)+abs(n)~=0) + As(m) = A(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) + A(T+1-i,T+1-j,T+1-k,T+1-l,T+1-n); + done(i+T+1,j+T+1,k+T+1,l+T+1,n+T+1) = 1; + done(T+1-i,T+1-j,T+1-k,T+1-l,T+1-n) = 1; + if ((i ~= n) || (j ~= l)) && (done(n+T+1,l+T+1,k+T+1,j+T+1,i+T+1) == 0) + As(m) = As(m) + A(n+T+1,l+T+1,k+T+1,j+T+1,i+T+1) + A(T+1-n,T+1-l,T+1-k,T+1-j,T+1-i); + done(n+T+1,l+T+1,k+T+1,j+T+1,i+T+1) = 1; + done(T+1-n,T+1-l,T+1-k,T+1-j,T+1-i) = 1; + end + m = m + 1; + end + end + end + end + end + end + ERR = 0; + end + otherwise + As = []; + fprintf(' Order of cooc is not in {1,2,3,4,5}.\n') +end + +if ERR == 1 + As = []; + fprintf('*** ERROR in symm: The number of elements in the array is not (2T+1)^order. ***\n') +end + + As = As(:); +function D = Residual(X,order,type) +% Computes the noise residual of a given type and order from MxN image X. +% residual order \in {1,2,3,4,5,6} +% type \in {hor,ver,diag,mdiag,KB,edge-h,edge-v,edge-d,edge-m} +% The resulting residual is an (M-b)x(N-b) array of the specified order, +% where b = ceil(order/2). This cropping is little more than it needs to +% be to make sure all the residuals are easily "synchronized". +% !!!!!!!!!!!!! Use order = 2 with KB and all edge residuals !!!!!!!!!!!!! + +[M N] = size(X); +I = 1+ceil(order/2) : M-ceil(order/2); +J = 1+ceil(order/2) : N-ceil(order/2); + +switch type + case 'hor' + switch order + case 1, D = - X(I,J) + X(I,J+1); + case 2, D = X(I,J-1) - 2*X(I,J) + X(I,J+1); + case 3, D = X(I,J-1) - 3*X(I,J) + 3*X(I,J+1) - X(I,J+2); + case 4, D = -X(I,J-2) + 4*X(I,J-1) - 6*X(I,J) + 4*X(I,J+1) - X(I,J+2); + case 5, D = -X(I,J-2) + 5*X(I,J-1) - 10*X(I,J) + 10*X(I,J+1) - 5*X(I,J+2) + X(I,J+3); + case 6, D = X(I,J-3) - 6*X(I,J-2) + 15*X(I,J-1) - 20*X(I,J) + 15*X(I,J+1) - 6*X(I,J+2) + X(I,J+3); + end + case 'ver' + switch order + case 1, D = - X(I,J) + X(I+1,J); + case 2, D = X(I-1,J) - 2*X(I,J) + X(I+1,J); + case 3, D = X(I-1,J) - 3*X(I,J) + 3*X(I+1,J) - X(I+2,J); + case 4, D = -X(I-2,J) + 4*X(I-1,J) - 6*X(I,J) + 4*X(I+1,J) - X(I+2,J); + case 5, D = -X(I-2,J) + 5*X(I-1,J) - 10*X(I,J) + 10*X(I+1,J) - 5*X(I+2,J) + X(I+3,J); + case 6, D = X(I-3,J) - 6*X(I-2,J) + 15*X(I-1,J) - 20*X(I,J) + 15*X(I+1,J) - 6*X(I+2,J) + X(I+3,J); + end + case 'diag' + switch order + case 1, D = - X(I,J) + X(I+1,J+1); + case 2, D = X(I-1,J-1) - 2*X(I,J) + X(I+1,J+1); + case 3, D = X(I-1,J-1) - 3*X(I,J) + 3*X(I+1,J+1) - X(I+2,J+2); + case 4, D = -X(I-2,J-2) + 4*X(I-1,J-1) - 6*X(I,J) + 4*X(I+1,J+1) - X(I+2,J+2); + case 5, D = -X(I-2,J-2) + 5*X(I-1,J-1) - 10*X(I,J) + 10*X(I+1,J+1) - 5*X(I+2,J+2) + X(I+3,J+3); + case 6, D = X(I-3,J-3) - 6*X(I-2,J-2) + 15*X(I-1,J-1) - 20*X(I,J) + 15*X(I+1,J+1) - 6*X(I+2,J+2) + X(I+3,J+3); + end + case 'mdiag' + switch order + case 1, D = - X(I,J) + X(I-1,J+1); + case 2, D = X(I-1,J+1) - 2*X(I,J) + X(I+1,J-1); + case 3, D = X(I-1,J+1) - 3*X(I,J) + 3*X(I+1,J-1) - X(I+2,J-2); + case 4, D = -X(I-2,J+2) + 4*X(I-1,J+1) - 6*X(I,J) + 4*X(I+1,J-1) - X(I+2,J-2); + case 5, D = -X(I-2,J+2) + 5*X(I-1,J+1) - 10*X(I,J) + 10*X(I+1,J-1) - 5*X(I+2,J-2) + X(I+3,J-3); + case 6, D = X(I-3,J+3) - 6*X(I-2,J+2) + 15*X(I-1,J+1) - 20*X(I,J) + 15*X(I+1,J-1) - 6*X(I+2,J-2) + X(I+3,J-3); + end + case 'KB' + D = -X(I-1,J-1) + 2*X(I-1,J) - X(I-1,J+1) + 2*X(I,J-1) - 4*X(I,J) + 2*X(I,J+1) - X(I+1,J-1) + 2*X(I+1,J) - X(I+1,J+1); + case 'edge-h' + Du = 2*X(I-1,J) + 2*X(I,J-1) + 2*X(I,J+1) - X(I-1,J-1) - X(I-1,J+1) - 4*X(I,J); % -1 2 -1 + Db = 2*X(I+1,J) + 2*X(I,J-1) + 2*X(I,J+1) - X(I+1,J-1) - X(I+1,J+1) - 4*X(I,J); % 2 C 2 + flipped vertically + D = [Du,Db]; + case 'edge-v' + Dl = 2*X(I,J-1) + 2*X(I-1,J) + 2*X(I+1,J) - X(I-1,J-1) - X(I+1,J-1) - 4*X(I,J); % -1 2 + Dr = 2*X(I,J+1) + 2*X(I-1,J) + 2*X(I+1,J) - X(I-1,J+1) - X(I+1,J+1) - 4*X(I,J); % 2 C + flipped horizontally + D = [Dl,Dr]; % -1 2 + case 'edge-m' + Dlu = 2*X(I,J-1) + 2*X(I-1,J) - X(I-1,J-1) - X(I+1,J-1) - X(I-1,J+1) - X(I,J); % -1 2 -1 + Drb = 2*X(I,J+1) + 2*X(I+1,J) - X(I+1,J+1) - X(I+1,J-1) - X(I-1,J+1) - X(I,J); % 2 C + flipped mdiag + D = [Dlu,Drb]; % -1 + case 'edge-d' + Dru = 2*X(I-1,J) + 2*X(I,J+1) - X(I-1,J+1) - X(I-1,J-1) - X(I+1,J+1) - X(I,J); % -1 2 -1 + Dlb = 2*X(I,J-1) + 2*X(I+1,J) - X(I+1,J-1) - X(I+1,J+1) - X(I-1,J-1) - X(I,J); % C 2 + flipped diag + D = [Dru,Dlb]; % -1 + case 'KV' + D = 8*X(I-1,J) + 8*X(I+1,J) + 8*X(I,J-1) + 8*X(I,J+1); + D = D - 6*X(I-1,J+1) - 6*X(I-1,J-1) - 6*X(I+1,J-1) - 6*X(I+1,J+1); + D = D - 2*X(I-2,J) - 2*X(I+2,J) - 2*X(I,J+2) - 2*X(I,J-2); + D = D + 2*X(I-1,J-2) + 2*X(I-2,J-1) + 2*X(I-2,J+1) + 2*X(I-1,J+2) + 2*X(I+1,J+2) + 2*X(I+2,J+1) + 2*X(I+2,J-1) + 2*X(I+1,J-2); + D = D - X(I-2,J-2) - X(I-2,J+2) - X(I+2,J-2) - X(I+2,J+2) - 12*X(I,J); +end diff --git a/富模型空域图像隐写分析(SRM)/提取特征/SubmodelConcatenation.m b/富模型空域图像隐写分析(SRM)/提取特征/SubmodelConcatenation.m new file mode 100644 index 0000000..9b2c4e7 --- /dev/null +++ b/富模型空域图像隐写分析(SRM)/提取特征/SubmodelConcatenation.m @@ -0,0 +1,12 @@ +function fea = SubmodelConcatenation(F) +Ss = fieldnames(F); +indexTo = 0; + for Sid = 1:length(Ss) + Fsingle = eval(['F.' Ss{Sid}]); + indexFrom = indexTo + 1; +% fprintf(' F.%s : %d x %d\n', Ss{Sid}, size(Fsingle, 1), size(Fsingle, 2)); + indexTo = indexFrom + size(Fsingle,2) - 1; + fea(1,indexFrom:indexTo) = Fsingle; + end +end + diff --git a/富模型空域图像隐写分析(SRM)/集成分类器/SRM_cover.mat b/富模型空域图像隐写分析(SRM)/集成分类器/SRM_cover.mat new file mode 100644 index 0000000..3160600 Binary files /dev/null and b/富模型空域图像隐写分析(SRM)/集成分类器/SRM_cover.mat differ diff --git a/富模型空域图像隐写分析(SRM)/集成分类器/SRM_stego.mat b/富模型空域图像隐写分析(SRM)/集成分类器/SRM_stego.mat new file mode 100644 index 0000000..a9d012c Binary files /dev/null and b/富模型空域图像隐写分析(SRM)/集成分类器/SRM_stego.mat differ diff --git a/富模型空域图像隐写分析(SRM)/集成分类器/ensemble_testing.m b/富模型空域图像隐写分析(SRM)/集成分类器/ensemble_testing.m new file mode 100644 index 0000000..d0d6ef2 --- /dev/null +++ b/富模型空域图像隐写分析(SRM)/集成分类器/ensemble_testing.m @@ -0,0 +1,55 @@ +function results = ensemble_testing(X,trained_ensemble) +% ------------------------------------------------------------------------- +% Ensemble Classification | June 2013 | version 2.0 | TESTING ROUTINE +% ------------------------------------------------------------------------- +% INPUT: +% - X - testing features (in a row-by-row manner) +% - trained_ensemble - trained ensemble - cell array of individual base +% learners (output of the 'ensemble_training' routine) +% OUTPUT: +% - results.predictions - individual cover (-1) and stego (+1) predictions +% based on the majority voting scheme +% - results.votes - sum of all votes (gives some information about +% prediction confidence) +% ------------------------------------------------------------------------- +% Please see the main routine 'ensemble_training' for more information. +% ------------------------------------------------------------------------- +% Copyright (c) 2013 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013 +% http://dde.binghamton.edu/download/ensemble +% ------------------------------------------------------------------------- +% References: +% [1] - J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for +% steganalysis of digital media. IEEE Transactions on Information Forensics +% and Security. Currently under review. +% ------------------------------------------------------------------------- + +% simple majority voting scheme +votes = zeros(size(X,1),1); +for i = 1:length(trained_ensemble) + proj = X(:,trained_ensemble{i}.subspace)*trained_ensemble{i}.w-trained_ensemble{i}.b; + votes = votes+sign(proj); +end + +% resolve ties randomly +votes(votes==0) = rand(sum(votes==0),1)-0.5; +% form final predictions +results.predictions = sign(votes); +% output also the sum of the individual votes (~confidence info) +results.votes = votes; diff --git a/富模型空域图像隐写分析(SRM)/集成分类器/ensemble_training.m b/富模型空域图像隐写分析(SRM)/集成分类器/ensemble_training.m new file mode 100644 index 0000000..78b8a82 --- /dev/null +++ b/富模型空域图像隐写分析(SRM)/集成分类器/ensemble_training.m @@ -0,0 +1,609 @@ +function [trained_ensemble,results] = ensemble_training(Xc,Xs,settings) +% ------------------------------------------------------------------------- +% Ensemble Classification | June 2013 | version 2.0 +% ------------------------------------------------------------------------- +% The purpose of version 2.0 is to simplify everything as much as possible. +% Here is a list of the main modifications compared to the first version of +% the ensemble classifier: +% - Instead of a single routine, we separated training form testing. This +% allows for more flexibility in the usage. +% - Training outputs the data structure 'trained_ensemble' which allows +% for easy storing of the trained classifier. +% - Ensemble now doesn't accept paths to features any more. Instead, it +% requires the features directly (Xc - cover features, Xs - stego +% features). Xc and Xs must have the same dimension and must contain +% synchronized cover/stego pairs - see the attached tutorial for more +% details on this. +% - There is no output into a log file. So there is no hard-drive access +% at all now. +% - Since the training and testing routines were separated, our ensemble +% implementation no longer takes care of training/testing divisions. +% This is the responsibility of the user now. Again, see the attached +% tutorial for examples. +% - Bagging is now always on +% - We fixed the fclose bug (Error: too many files open) +% - Covariance caching option was removed +% - Added settings.verbose = 2 option (screen output of only the last row) +% - Ensemble now works even if full dimension is equal to 1 or 2. If equal +% to 1, multiple decisions are still combined as different base learners +% are trained on different bootstrap samples (bagging). +% ------------------------------------------------------------------------- +% Copyright (c) 2013 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013 +% http://dde.binghamton.edu/download/ensemble +% ------------------------------------------------------------------------- +% References: +% [1] - J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for +% steganalysis of digital media. IEEE Transactions on Information Forensics +% and Security. Currently under review. +% ------------------------------------------------------------------------- +% INPUT: +% Xc - cover features in a row-by-row manner +% Xs - corresponding stego features (needs to be synchronized!) +% settings +% .seed_subspaces (default = random) - PRNG seed for random subspace +% generation +% .seed_bootstrap (default = random) - PRNG seed for bootstrap samples +% generation +% .d_sub (default = 'automatic') - random subspace dimensionality; either +% an integer (e.g. 200) or the string 'automatic' is accepted; in +% the latter case, an automatic search for the optimal subspace +% dimensionality is performed, see [1] for more details +% .L (default = 'automatic') - number of random subspaces / base +% learners; either an integer (e.g. 50) or the string 'automatic' +% is accepted; in the latter case, an automatic stopping criterion +% is used, see [1] for more details +% .verbose (default = 1) - turn on/off screen output +% = 0 ... no screen output +% = 1 ... full screen output +% = 2 ... screen output of only the last row (results) +% +% Parameters for the search for d_sub (when .d_sub = 'automatic'): +% +% .k_step (default = 200) - initial step for d_sub when searching from +% left (stage 1 of Algorithm 2 in [1]) +% .Eoob_tolerance (default = 0.02) - the relative tolerance for the +% minimality of OOB within the search, i.e. specifies the stopping +% criterion for the stage 2 in Algorithm 2 +% +% Both default parameters work well for most of the steganalysis scenarios. +% +% Parameters for automatic stopping criterion for L (when .L ='automatic'); +% see [1] for more details: +% +% .L_kernel (default = ones(1,5)/5) - over how many values of OOB +% estimates is the moving average taken over +% .L_min_length (default = 25) - the minimum number of random subspaces +% that will be generated +% .L_memory (default = 50) - how many last OOB estimates need to stay in +% the epsilon tube +% .L_epsilon (default = 0.005) - specification of the epsilon tube +% +% According to our experiments, these values are sufficient for most of the +% steganalysis tasks (different algorithms and features). Nevertheless, any +% of these parameters can be modified before calling the ensemble if +% desired. +% ------------------------------------------------------------------------- +% OUTPUT: +% trained_ensemble - cell array of individual FLD base learners, each +% containing the following three fields: +% - subspace - random subspace indices +% - w - vector of weights (normal vector to the decision boundary) +% - b - bias +% results - data structure with additional results of the training +% procedure (training time, progress of the OOB error estimate, +% summary of the search for d_sub, etc. See the attached tutorial +% where we use some of these pieces of information for demonstrative +% purposes +% ------------------------------------------------------------------------- + + +if ~exist('settings','var'), settings.all_default = 1; end + +% check settings, set default values, initial screen print +[Xc,Xs,settings] = check_initial_setup(Xc,Xs,settings); + +% initialization of the search for d_sub +[SEARCH,settings,search_counter,MIN_OOB,OOB.error] = initialize_search(settings); + +% search loop (if search for d_sub is to be executed) +while SEARCH.in_progress + search_counter = search_counter+1; + + % initialization + [SEARCH.start_time_current_d_sub,i,next_random_subspace,TXT,base_learner] = deal(tic,0,1,'',cell(settings.max_number_base_learners,1)); + + % loop over individual base learners + while next_random_subspace + i = i+1; + + %%% RANDOM SUBSPACE GENERATION + base_learner{i}.subspace = generate_random_subspace(settings.randstream.subspaces,settings.max_dim,settings.d_sub); + + %%% BOOTSTRAP INITIALIZATION + OOB = bootstrap_initialization(Xc,Xs,OOB,settings); + + %%% TRAINING PHASE + base_learner{i} = FLD_training(Xc,Xs,base_learner{i},OOB,settings); + + %%% OOB ERROR ESTIMATION + OOB = update_oob_error_estimates(Xc,Xs,base_learner{i},OOB,i); + + [next_random_subspace,MSG] = getFlag_nextRandomSubspace(i,OOB,settings); + + % SCREEN OUTPUT + CT = double(toc(SEARCH.start_time_current_d_sub)); + TXT = updateTXT(TXT,sprintf(' - d_sub %s : OOB %.4f : L %i : T %.1f sec%s',k_to_string(settings.d_sub),OOB.error,i,CT,MSG),settings); + + end % while next_random_subspace + + results.search.d_sub(search_counter) = settings.d_sub; + updateLog_swipe(settings,'\n'); + + if OOB.error terminate search + SEARCH.in_progress = 0; + SEARCH.optimal_d_sub = SEARCH.x(SEARCH.E==MINIMAL_ERROR); + SEARCH.optimal_d_sub = SEARCH.optimal_d_sub(1); + return; +end + + +if minE_id == 1 + % smallest k is the best => reduce step + SEARCH.step = floor(SEARCH.step/2); + SEARCH = add_gridpoints(SEARCH,SEARCH.x(1)+SEARCH.step*[-1 1]); +elseif minE_id == length(SEARCH.x) + % largest k is the best + if SEARCH.x(end) + SEARCH.step <= settings.max_dim && (min(abs(SEARCH.x(end) + SEARCH.step-SEARCH.x))>SEARCH.step/2) + % continue to the right + SEARCH = add_gridpoints(SEARCH,SEARCH.x(end) + SEARCH.step); + else + % hitting the full dimensionality + if (MINIMAL_ERROR/SEARCH.E(end-1) >= 1 - settings.Eoob_tolerance) ... % desired tolerance fulfilled + || SEARCH.E(end-1)-MINIMAL_ERROR < 5e-3 ... % maximal precision in terms of error set to 0.5% + || SEARCH.stepSEARCH.step/2) ... % one more step to the right is not too close to any other point + && ~(SEARCH.E(end)>SEARCH.E(end-1) && SEARCH.E(end)>SEARCH.E(end-2)) % the last point is not worse than the two previous ones + % robustness ensurance, try one more step to the right + SEARCH = add_gridpoints(SEARCH,settings.d_sub + SEARCH.step); +else + % best k is not at the edge of the grid (and robustness is resolved) + err_around = mean(SEARCH.E(minE_id+[-1 1])); + if (MINIMAL_ERROR/err_around >= 1 - settings.Eoob_tolerance) ... % desired tolerance fulfilled + || err_around-MINIMAL_ERROR < 5e-3 ... % maximal precision in terms of error set to 0.5% + || SEARCH.step= settings.max_dim/4, settings.d_sub_step = floor(settings.max_dim/4); end + if settings.max_dim < 10, settings.d_sub_step = 1; end + SEARCH.x = settings.d_sub_step*[1 2 3]; + if settings.max_dim==2, SEARCH.x = [1 2]; end + SEARCH.E = -ones(size(SEARCH.x)); + SEARCH.terminate = 0; + SEARCH.step = settings.d_sub_step; + settings.d_sub = SEARCH.x(1); +end + +search_counter = 0; +MIN_OOB = 1; +OOB_error = 1; + +function TXT = updateTXT(old,TXT,settings) +if isfield(settings,'kmin') + if length(TXT)>3 + if ~strcmp(TXT(1:3),' - ') + TXT = [' - ' TXT]; + end + end +end +if settings.verbose==1 + if exist('/home','dir') + % do not delete on cluster, it displays incorrectly when writing through STDOUT into file + fprintf(['\n' TXT]); + else + fprintf([repmat('\b',1,length(old)) TXT]); + end +end + +function s = k_to_string(k) +if length(k)==1 + s = num2str(k); + return; +end + +s=['[' num2str(k(1))]; +for i=2:length(k) + s = [s ',' num2str(k(i))]; %#ok +end +s = [s ']']; + +function updateLog_swipe(settings,TXT,final) +if ~exist('final','var'), final=0; end +if settings.verbose==1 || (settings.verbose==2 && final==1), fprintf(TXT); end + +function OOB = bootstrap_initialization(Xc,Xs,OOB,settings) +% initialization of the structure for OOB error estimates +OOB.SUB = floor(size(Xc,1)*rand(settings.randstream.bootstrap,size(Xc,1),1))+1; +OOB.ID = setdiff(1:size(Xc,1),OOB.SUB); +if ~isfield(OOB,'Xc') + OOB.Xc.fusion_majority_vote = zeros(size(Xc,1),1); % majority voting fusion + OOB.Xc.num = zeros(size(Xc,1),1); % number of fused votes + OOB.Xs.fusion_majority_vote = zeros(size(Xs,1),1); % majority voting fusion + OOB.Xs.num = zeros(size(Xs,1),1); % number of fused votes +end +if ~isfield(OOB,'randstream_for_ties') + % Doesn't really matter that we fix the seed here. This will be used + % only for resolving voting ties. We are fixing this in order to make + % all results nicely reproducible. + OOB.randstream_for_ties = RandStream('mt19937ar','Seed',1); +end + + +function [base_learner] = findThreshold(Xm,Xp,base_learner) +% find threshold through minimizing (MD+FA)/2, where MD stands for the +% missed detection rate and FA for the false alarms rate +P1 = Xm*base_learner.w + sqrt(1)*randn(size(Xm,1),1); +P2 = Xp*base_learner.w + sqrt(1)*randn(size(Xm,1),1); +L = [-ones(size(Xm,1),1);ones(size(Xp,1),1)]; +[P,IX] = sort([P1;P2]); +L = L(IX); +Lm = (L==-1); +sgn = 1; + +MD = 0; +FA = sum(Lm); +MD2=FA; +FA2=MD; +Emin = (FA+MD); +Eact = zeros(size(L-1)); +Eact2 = Eact; +for idTr=1:length(P)-1 + if L(idTr)==-1 + FA=FA-1; + MD2=MD2+1; + else + FA2=FA2-1; + MD=MD+1; + end + Eact(idTr) = FA+MD; + Eact2(idTr) = FA2+MD2; + if Eact(idTr)0)+sum(TMP_s<0))/(length(TMP_c)+length(TMP_s)); + +if ~ischar(OOB) && ~isempty(OOB) + H = hist([OOB.Xc.num;OOB.Xs.num],0:max([OOB.Xc.num;OOB.Xs.num])); + avg_L = sum(H.*(0:length(H)-1))/sum(H); % average L in OOB + OOB.x(i) = avg_L; + OOB.y(i) = OOB.error; +end + +function base_learner = FLD_training(Xc,Xs,base_learner,OOB,settings) +% FLD TRAINING +Xm = Xc(OOB.SUB,base_learner.subspace); +Xp = Xs(OOB.SUB,base_learner.subspace); + +% remove constants +remove = false(1,size(Xm,2)); +adepts = unique([find(Xm(1,:)==Xm(2,:)) find(Xp(1,:)==Xp(2,:))]); +for ad_id = adepts + U1=unique(Xm(:,ad_id)); + if numel(U1)==1 + U2=unique(Xp(:,ad_id)); + if numel(U2)==1, if U1==U2, remove(ad_id) = true; end; end + end +end + +muC = sum(Xm,1); muC = double(muC)/size(Xm,1); +muS = sum(Xp,1); muS = double(muS)/size(Xp,1); +mu = (muS-muC)'; + +% calculate sigC +xc = bsxfun(@minus,Xm,muC); +sigC = xc'*xc; +sigC = double(sigC)/size(Xm,1); + +% calculate sigS +xc = bsxfun(@minus,Xp,muS); +sigS = xc'*xc; +sigS = double(sigS)/size(Xp,1); + +sigCS = sigC + sigS; + +% regularization +sigCS = sigCS + 1e-10*eye(size(sigC,1)); + +% check for NaN values (may occur when the feature value is constant over images) +nan_values = sum(isnan(sigCS))>0; +nan_values = nan_values | remove; + +sigCS = sigCS(~nan_values,~nan_values); +mu = mu(~nan_values); +lastwarn(''); +warning('off','MATLAB:nearlySingularMatrix'); +warning('off','MATLAB:singularMatrix'); +base_learner.w = sigCS\mu; +% regularization (if necessary) +[txt,warnid] = lastwarn(); %#ok +while strcmp(warnid,'MATLAB:singularMatrix') || (strcmp(warnid,'MATLAB:nearlySingularMatrix') && ~settings.ignore_nearly_singular_matrix_warning) + lastwarn(''); + if ~exist('counter','var'), counter=1; else counter = counter*5; end + sigCS = sigCS + counter*eps*eye(size(sigCS,1)); + base_learner.w = sigCS\mu; + [txt,warnid] = lastwarn(); %#ok +end +warning('on','MATLAB:nearlySingularMatrix'); +warning('on','MATLAB:singularMatrix'); +if length(sigCS)~=length(sigC) + % resolve previously found NaN values, set the corresponding elements of w equal to zero + w_new = zeros(length(sigC),1); + w_new(~nan_values) = base_learner.w; + base_learner.w = w_new; +end + +% find threshold to minimize FA+MD +[base_learner] = findThreshold(Xm,Xp,base_learner); + +function results = add_search_info(results,settings,search_counter,SEARCH,i,CT) +% update information about d_sub search +if settings.search_for_d_sub + results.search.OOB(search_counter) = SEARCH.E(SEARCH.x==results.search.d_sub(search_counter)); + results.search.L(search_counter) = i; + results.search.time(search_counter) = CT; +end + +function SEARCH = add_gridpoints(SEARCH,points) +% add new points for the search for d_sub +for point=points + if SEARCH.x(1)>point + SEARCH.x = [point SEARCH.x]; + SEARCH.E = [-1 SEARCH.E]; + continue; + end + if SEARCH.x(end) wetCost) = wetCost; % threshold on the costs +rho(isnan(rho)) = wetCost; % if all xi{} are zero threshold the cost +rhoP1 = rho; +rhoM1 = rho; +rhoP1(cover==255) = wetCost; % do not embed +1 if the pixel has max value +rhoM1(cover==0) = wetCost; % do not embed -1 if the pixel has min value + +stego = EmbeddingSimulator(cover,rhoP1,rhoM1,payload*numel(cover),false); +end + +function cost = f_cal_cost_HILL(cover) +HF=[-1 2 -1;2 -4 2;-1 2 -1]; +H2 = fspecial('average',[3 3]); +% Get cost +cover=double(cover); +sizeCover=size(cover); +padsize=max(size(HF)); +coverPadded = padarray(cover, [padsize padsize], 'symmetric');% add padding +R = conv2(coverPadded,HF, 'same');%mirror-padded convolution +W=conv2(abs(R),H2,'same'); +% correct the W shift if filter size is even +if mod(size(HF, 1), 2) == 0, W = circshift(W, [1, 0]); end; +if mod(size(HF, 2), 2) == 0, W = circshift(W, [0, 1]); end; +% remove padding +W = W(((size(W, 1)-sizeCover(1))/2)+1:end-((size(W, 1)-sizeCover(1))/2), ((size(W, 2)-sizeCover(2))/2)+1:end-((size(W, 2)-sizeCover(2))/2)); +cost=1./(W+10^(-10)); +wetCost = 10^10; +% compute embedding costs \rho +rhoA = cost; +rhoA(rhoA > wetCost) = wetCost; % threshold on the costs +rhoA(isnan(rhoA)) = wetCost; % if all xi{} are zero threshold the cost +HW = fspecial('average', [15, 15]) ; +cost = imfilter(rhoA, HW ,'symmetric','same'); +end + +function [y] = EmbeddingSimulator(x, rhoP1, rhoM1, m, fixEmbeddingChanges) + n = numel(x); + lambda = calc_lambda(rhoP1, rhoM1, m, n); + pChangeP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pChangeM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + if fixEmbeddingChanges == 1 + RandStream.setGlobalStream(RandStream('mt19937ar','seed',139187)); + else + RandStream.setGlobalStream(RandStream('mt19937ar','Seed',sum(100*clock))); + end + randChange = rand(size(x)); + y = x; + y(randChange < pChangeP1) = y(randChange < pChangeP1) + 1; + y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) = y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) - 1; + + function lambda = calc_lambda(rhoP1, rhoM1, message_length, n) + + l3 = 1e+3; + m3 = double(message_length + 1); + iterations = 0; + while m3 > message_length + l3 = l3 * 2; + pP1 = (exp(-l3 .* rhoP1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + pM1 = (exp(-l3 .* rhoM1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + m3 = ternary_entropyf(pP1, pM1); + iterations = iterations + 1; + if (iterations > 10) + lambda = l3; + return; + end + end + l1 = 0; + m1 = double(n); + lambda = 0; + alpha = double(message_length)/n; + % limit search to 30 iterations + % and require that relative payload embedded is roughly within 1/1000 of the required relative payload + while (double(m1-m3)/n > alpha/1000.0 ) && (iterations<30) + lambda = l1+(l3-l1)/2; + pP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + m2 = ternary_entropyf(pP1, pM1); + if m2 < message_length + l3 = lambda; + m3 = m2; + else + l1 = lambda; + m1 = m2; + end + iterations = iterations + 1; + end + end + + function Ht = ternary_entropyf(pP1, pM1) + p0 = 1-pP1-pM1; + P = [p0(:); pP1(:); pM1(:)]; + H = -((P).*log2(P)); + H((P 1-eps)) = 0; + Ht = sum(H); + end +end \ No newline at end of file diff --git a/空域隐写算法/S_UNIWARD1.m b/空域隐写算法/S_UNIWARD1.m new file mode 100644 index 0000000..7eb49af --- /dev/null +++ b/空域隐写算法/S_UNIWARD1.m @@ -0,0 +1,152 @@ +function stego = S_UNIWARD1(coverPath, payload) +% ------------------------------------------------------------------------- +% Copyright (c) 2013 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: vojtech_holub@yahoo.com | fridrich@binghamton.edu | October 2012 +% http://dde.binghamton.edu/download/steganography +% ------------------------------------------------------------------------- +% This function simulates embedding using S-UNIWARD steganographic +% algorithm. For more deatils about the individual submodels, please see +% the publication [1]. +% ------------------------------------------------------------------------- +% Input: coverPath ... path to the image +% payload ..... payload in bits per pixel +% Output: stego ....... resulting image with embedded payload +% ------------------------------------------------------------------------- +% PAPER +% ------------------------------------------------------------------------- + +sgm = 1; + +%% Get 2D wavelet filters - Daubechies 8 +% 1D high pass decomposition filter +hpdf = [-0.0544158422, 0.3128715909, -0.6756307363, 0.5853546837, 0.0158291053, -0.2840155430, -0.0004724846, 0.1287474266, 0.0173693010, -0.0440882539, ... + -0.0139810279, 0.0087460940, 0.0048703530, -0.0003917404, -0.0006754494, -0.0001174768]; +% 1D low pass decomposition filter +lpdf = (-1).^(0:numel(hpdf)-1).*fliplr(hpdf); +% construction of 2D wavelet filters +F{1} = lpdf'*hpdf; +F{2} = hpdf'*lpdf; +F{3} = hpdf'*hpdf; + +%% Get embedding costs +% inicialization +cover = double(imread(coverPath)); +wetCost = 10^8; +[k,l] = size(cover); + +% add padding +padSize = max([size(F{1})'; size(F{2})'; size(F{3})']); +coverPadded = padarray(cover, [padSize padSize], 'symmetric'); + +xi = cell(3, 1); +for fIndex = 1:3 + % compute residual + R = conv2(coverPadded, F{fIndex}, 'same'); + % compute suitability + xi{fIndex} = conv2(1./(abs(R)+sgm), rot90(abs(F{fIndex}), 2), 'same'); + % correct the suitability shift if filter size is even + if mod(size(F{fIndex}, 1), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [1, 0]); end; + if mod(size(F{fIndex}, 2), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [0, 1]); end; + % remove padding + xi{fIndex} = xi{fIndex}(((size(xi{fIndex}, 1)-k)/2)+1:end-((size(xi{fIndex}, 1)-k)/2), ((size(xi{fIndex}, 2)-l)/2)+1:end-((size(xi{fIndex}, 2)-l)/2)); +end + +% compute embedding costs \rho +rho = xi{1} + xi{2} + xi{3}; + +% adjust embedding costs +rho(rho > wetCost) = wetCost; % threshold on the costs +rho(isnan(rho)) = wetCost; % if all xi{} are zero threshold the cost +rhoP1 = rho; +rhoM1 = rho; +rhoP1(cover==255) = wetCost; % do not embed +1 if the pixel has max value +rhoM1(cover==0) = wetCost; % do not embed -1 if the pixel has min value + +%% Embedding simulator +stego = EmbeddingSimulator(cover, rhoP1, rhoM1, payload*numel(cover), false); + + +%% -------------------------------------------------------------------------------------------------------------------------- +% Embedding simulator simulates the embedding made by the best possible ternary coding method (it embeds on the entropy bound). +% This can be achieved in practice using "Multi-layered syndrome-trellis codes" (ML STC) that are asymptotically aproaching the bound. +function [y] = EmbeddingSimulator(x, rhoP1, rhoM1, m, fixEmbeddingChanges) + + n = numel(x); + lambda = calc_lambda(rhoP1, rhoM1, m, n); + pChangeP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pChangeM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + if fixEmbeddingChanges == 1 + RandStream.setGlobalStream(RandStream('mt19937ar','seed',139187)); + else + RandStream.setGlobalStream(RandStream('mt19937ar','Seed',sum(100*clock))); + end + randChange = rand(size(x)); + y = x; + y(randChange < pChangeP1) = y(randChange < pChangeP1) + 1; + y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) = y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) - 1; + + function lambda = calc_lambda(rhoP1, rhoM1, message_length, n) + + l3 = 1e+3; + m3 = double(message_length + 1); + iterations = 0; + while m3 > message_length + l3 = l3 * 2; + pP1 = (exp(-l3 .* rhoP1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + pM1 = (exp(-l3 .* rhoM1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + m3 = ternary_entropyf(pP1, pM1); + iterations = iterations + 1; + if (iterations > 10) + lambda = l3; + return; + end + end + + l1 = 0; + m1 = double(n); + lambda = 0; + + alpha = double(message_length)/n; + % limit search to 30 iterations + % and require that relative payload embedded is roughly within 1/1000 of the required relative payload + while (double(m1-m3)/n > alpha/1000.0 ) && (iterations<30) + lambda = l1+(l3-l1)/2; + pP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + m2 = ternary_entropyf(pP1, pM1); + if m2 < message_length + l3 = lambda; + m3 = m2; + else + l1 = lambda; + m1 = m2; + end + iterations = iterations + 1; + end + end + + function Ht = ternary_entropyf(pP1, pM1) + p0 = 1-pP1-pM1; + P = [p0(:); pP1(:); pM1(:)]; + H = -((P).*log2(P)); + H((P 1-eps)) = 0; + Ht = sum(H); + end +end +end \ No newline at end of file diff --git a/空域隐写算法/WOW.m b/空域隐写算法/WOW.m new file mode 100644 index 0000000..36e7ca4 --- /dev/null +++ b/空域隐写算法/WOW.m @@ -0,0 +1,156 @@ +function [stego, distortion] = WOW(cover, payload, params) +% ------------------------------------------------------------------------- +% Copyright (c) 2012 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: vojtech_holub@yahoo.com | fridrich@binghamton.edu | October 2012 +% http://dde.binghamton.edu/download/steganography +% ------------------------------------------------------------------------- +% This function simulates embedding using WOW steganographic +% algorithm. For more deatils about the individual submodels, please see +% the publication [1]. +% ------------------------------------------------------------------------- +% Input: coverPath ... path to the image +% payload ..... payload in bits per pixel +% Output: stego ....... resulting image with embedded payload +% ------------------------------------------------------------------------- +% [1] Designing Steganographic Distortion Using Directional Filters, +% V. Holub and J. Fridrich, to be presented at WIFS'12 IEEE International +% Workshop on Information Forensics and Security +% ------------------------------------------------------------------------- + +%% Get 2D wavelet filters - Daubechies 8 +% 1D high pass decomposition filter +hpdf = [-0.0544158422, 0.3128715909, -0.6756307363, 0.5853546837, 0.0158291053, -0.2840155430, -0.0004724846, 0.1287474266, 0.0173693010, -0.0440882539, ... + -0.0139810279, 0.0087460940, 0.0048703530, -0.0003917404, -0.0006754494, -0.0001174768]; +% 1D low pass decomposition filter +lpdf = (-1).^(0:numel(hpdf)-1).*fliplr(hpdf); +% construction of 2D wavelet filters +F{1} = lpdf'*hpdf; +F{2} = hpdf'*lpdf; +F{3} = hpdf'*hpdf; + +%% Get embedding costs +% inicialization +cover = double(cover); +p = params.p; +wetCost = 10^10; +sizeCover = size(cover); + +% add padding +padSize = max([size(F{1})'; size(F{2})'; size(F{3})']); +coverPadded = padarray(cover, [padSize padSize], 'symmetric'); + +% compute directional residual and suitability \xi for each filter +xi = cell(3, 1); +for fIndex = 1:3 + % compute residual + R = conv2(coverPadded, F{fIndex}, 'same'); + + % compute suitability + xi{fIndex} = conv2(abs(R), rot90(abs(F{fIndex}), 2), 'same'); + % correct the suitability shift if filter size is even + if mod(size(F{fIndex}, 1), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [1, 0]); end; + if mod(size(F{fIndex}, 2), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [0, 1]); end; + % remove padding + xi{fIndex} = xi{fIndex}(((size(xi{fIndex}, 1)-sizeCover(1))/2)+1:end-((size(xi{fIndex}, 1)-sizeCover(1))/2), ((size(xi{fIndex}, 2)-sizeCover(2))/2)+1:end-((size(xi{fIndex}, 2)-sizeCover(2))/2)); +end + +% compute embedding costs \rho +rho = ( (xi{1}.^p) + (xi{2}.^p) + (xi{3}.^p) ) .^ (-1/p); + +% adjust embedding costs +rho(rho > wetCost) = wetCost; % threshold on the costs +rho(isnan(rho)) = wetCost; % if all xi{} are zero threshold the cost +rhoP1 = rho; +rhoM1 = rho; +rhoP1(cover==255) = wetCost; % do not embed +1 if the pixel has max value +rhoM1(cover==0) = wetCost; % do not embed -1 if the pixel has min value + +%% Embedding simulator +stego = EmbeddingSimulator(cover, rhoP1, rhoM1, payload*numel(cover), false); +distortion_local = rho(cover~=stego); +distortion = sum(distortion_local); + +%% -------------------------------------------------------------------------------------------------------------------------- +% Embedding simulator simulates the embedding made by the best possible ternary coding method (it embeds on the entropy bound). +% This can be achieved in practice using "Multi-layered syndrome-trellis codes" (ML STC) that are asymptotically aproaching the bound. +function [y] = EmbeddingSimulator(x, rhoP1, rhoM1, m, fixEmbeddingChanges) + + n = numel(x); + lambda = calc_lambda(rhoP1, rhoM1, m, n); + pChangeP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pChangeM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + if fixEmbeddingChanges == 1 + RandStream.setGlobalStream(RandStream('mt19937ar','seed',139187)); + else + RandStream.setGlobalStream(RandStream('mt19937ar','Seed',sum(100*clock))); + end + randChange = rand(size(x)); + y = x; + y(randChange < pChangeP1) = y(randChange < pChangeP1) + 1; + y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) = y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) - 1; + + function lambda = calc_lambda(rhoP1, rhoM1, message_length, n) + + l3 = 1e+3; + m3 = double(message_length + 1); + iterations = 0; + while m3 > message_length + l3 = l3 * 2; + pP1 = (exp(-l3 .* rhoP1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + pM1 = (exp(-l3 .* rhoM1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + m3 = ternary_entropyf(pP1, pM1); + iterations = iterations + 1; + if (iterations > 10) + lambda = l3; + return; + end + end + + l1 = 0; + m1 = double(n); + lambda = 0; + + alpha = double(message_length)/n; + % limit search to 30 iterations + % and require that relative payload embedded is roughly within 1/1000 of the required relative payload + while (double(m1-m3)/n > alpha/1000.0 ) && (iterations<30) + lambda = l1+(l3-l1)/2; + pP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + m2 = ternary_entropyf(pP1, pM1); + if m2 < message_length + l3 = lambda; + m3 = m2; + else + l1 = lambda; + m1 = m2; + end + iterations = iterations + 1; + end + end + + function Ht = ternary_entropyf(pP1, pM1) + p0 = 1-pP1-pM1; + P = [p0(:); pP1(:); pM1(:)]; + H = -((P).*log2(P)); + H((P 1-eps)) = 0; + Ht = sum(H); + end +end +end diff --git a/选择信道感知隐写分析(SCA)/提取特征/1.pgm b/选择信道感知隐写分析(SCA)/提取特征/1.pgm new file mode 100644 index 0000000..3bf4834 --- /dev/null +++ b/选择信道感知隐写分析(SCA)/提取特征/1.pgm @@ -0,0 +1,4 @@ +P5 +512 512 +255 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\ No newline at end of file diff --git a/选择信道感知隐写分析(SCA)/提取特征/S_UNIWARD.m b/选择信道感知隐写分析(SCA)/提取特征/S_UNIWARD.m new file mode 100644 index 0000000..40bf981 --- /dev/null +++ b/选择信道感知隐写分析(SCA)/提取特征/S_UNIWARD.m @@ -0,0 +1,163 @@ +function [stego, Ps] = S_UNIWARD(cover, payload, sgm) +% ------------------------------------------------------------------------- +% Copyright (c) 2014 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: vojtech_holub@yahoo.com | fridrich@binghamton.edu | October 2012 +% http://dde.binghamton.edu/download/steganography +% ------------------------------------------------------------------------- +% This function simulates embedding using S-UNIWARD steganographic +% algorithm. For more deatils about the individual submodels, please see +% the publication [1]. +% This particular version of the simulator was slightly edited to accept +% custom values of the stabilization constant sigma and to output also the +% matrix of embedding probabilities for each pixel. +% ------------------------------------------------------------------------- +% Input: coverPath ... path to the image +% payload ..... payload in bits per pixel +% sgm ......... stabilization constant +% Output: stego ....... resulting image with embedded payload +% Ps .......... matrix of embedding probabilities for each pixel +% ------------------------------------------------------------------------- +% [1] Digital Image Steganography Using Universal Distortion, V. Holub and +% J. Fridrich, Proc. ACM Workshop on Information Hiding and Multimedia +% Security, 2013 +% http://dde.binghamton.edu/vholub/pdf/ ... +% ACMIH2013_Digital_Image_Steganography_Using_Universal_Distortion.pdf +% ------------------------------------------------------------------------- + +% sgm = 10*eps; + +%% Get 2D wavelet filters - Daubechies 8 +% 1D high pass decomposition filter +hpdf = [-0.0544158422, 0.3128715909, -0.6756307363, 0.5853546837, 0.0158291053, -0.2840155430, -0.0004724846, 0.1287474266, 0.0173693010, -0.0440882539, ... + -0.0139810279, 0.0087460940, 0.0048703530, -0.0003917404, -0.0006754494, -0.0001174768]; +% 1D low pass decomposition filter +lpdf = (-1).^(0:numel(hpdf)-1).*fliplr(hpdf); +% construction of 2D wavelet filters +F{1} = lpdf'*hpdf; +F{2} = hpdf'*lpdf; +F{3} = hpdf'*hpdf; + +%% Get embedding costs +% inicialization +% cover = double(imread(coverPath)); +wetCost = 10^8; +[k,l] = size(cover); + +% add padding +padSize = max([size(F{1})'; size(F{2})'; size(F{3})']); +coverPadded = padarray(cover, [padSize padSize], 'symmetric'); + +xi = cell(3, 1); +for fIndex = 1:3 + % compute residual + R = conv2(coverPadded, F{fIndex}, 'same'); + % compute suitability + xi{fIndex} = conv2(1./(abs(R)+sgm), rot90(abs(F{fIndex}), 2), 'same'); + % correct the suitability shift if filter size is even + if mod(size(F{fIndex}, 1), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [1, 0]); end; + if mod(size(F{fIndex}, 2), 2) == 0, xi{fIndex} = circshift(xi{fIndex}, [0, 1]); end; + % remove padding + xi{fIndex} = xi{fIndex}(((size(xi{fIndex}, 1)-k)/2)+1:end-((size(xi{fIndex}, 1)-k)/2), ((size(xi{fIndex}, 2)-l)/2)+1:end-((size(xi{fIndex}, 2)-l)/2)); +end + +% compute embedding costs \rho +rho = xi{1} + xi{2} + xi{3}; + +% adjust embedding costs +rho(rho > wetCost) = wetCost; % threshold on the costs +rho(isnan(rho)) = wetCost; % if all xi{} are zero threshold the cost +rhoP1 = rho; +rhoM1 = rho; +rhoP1(cover==255) = wetCost; % do not embed +1 if the pixel has max value +rhoM1(cover==0) = wetCost; % do not embed -1 if the pixel has min value + +%% Embedding simulator +[stego, Ps] = EmbeddingSimulator(cover, rhoP1, rhoM1, payload*numel(cover), false); + + +%% -------------------------------------------------------------------------------------------------------------------------- +% Embedding simulator simulates the embedding made by the best possible ternary coding method (it embeds on the entropy bound). +% This can be achieved in practice using "Multi-layered syndrome-trellis codes" (ML STC) that are asymptotically aproaching the bound. +function [y, Ps] = EmbeddingSimulator(x, rhoP1, rhoM1, m, fixEmbeddingChanges) + + n = numel(x); + lambda = calc_lambda(rhoP1, rhoM1, m, n); + pChangeP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pChangeM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + if fixEmbeddingChanges == 1 + RandStream.setGlobalStream(RandStream('mt19937ar','seed',139187)); + else + RandStream.setGlobalStream(RandStream('mt19937ar','Seed',sum(100*clock))); + end + randChange = rand(size(x)); + y = x; + y(randChange < pChangeP1) = y(randChange < pChangeP1) + 1; + y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) = y(randChange >= pChangeP1 & randChange < pChangeP1+pChangeM1) - 1; + + Ps = pChangeP1 + pChangeM1; + + function lambda = calc_lambda(rhoP1, rhoM1, message_length, n) + + l3 = 1e+3; + m3 = double(message_length + 1); + iterations = 0; + while m3 > message_length + l3 = l3 * 2; + pP1 = (exp(-l3 .* rhoP1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + pM1 = (exp(-l3 .* rhoM1))./(1 + exp(-l3 .* rhoP1) + exp(-l3 .* rhoM1)); + m3 = ternary_entropyf(pP1, pM1); + iterations = iterations + 1; + if (iterations > 10) + lambda = l3; + return; + end + end + + l1 = 0; + m1 = double(n); + lambda = 0; + + alpha = double(message_length)/n; + % limit search to 30 iterations + % and require that relative payload embedded is roughly within 1/1000 of the required relative payload + while (double(m1-m3)/n > alpha/1000.0 ) && (iterations<30) + lambda = l1+(l3-l1)/2; + pP1 = (exp(-lambda .* rhoP1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + pM1 = (exp(-lambda .* rhoM1))./(1 + exp(-lambda .* rhoP1) + exp(-lambda .* rhoM1)); + m2 = ternary_entropyf(pP1, pM1); + if m2 < message_length + l3 = lambda; + m3 = m2; + else + l1 = lambda; + m1 = m2; + end + iterations = iterations + 1; + end + end + + function Ht = ternary_entropyf(pP1, pM1) + p0 = 1-pP1-pM1; + P = [p0(:); pP1(:); pM1(:)]; + H = -((P).*log2(P)); + H((P 1-eps)) = 0; + Ht = sum(H); + end +end +end \ No newline at end of file diff --git a/选择信道感知隐写分析(SCA)/提取特征/S_uniward_pro.m b/选择信道感知隐写分析(SCA)/提取特征/S_uniward_pro.m new file mode 100644 index 0000000..19ecad6 --- /dev/null +++ b/选择信道感知隐写分析(SCA)/提取特征/S_uniward_pro.m @@ -0,0 +1,9 @@ +function weight=S_uniward_pro(img,payload) + +sigma_bar = 10*eps; % testing stabilization constant +img=double(img); +[~, weight] = S_UNIWARD(img, payload, sigma_bar); + + + + diff --git a/选择信道感知隐写分析(SCA)/提取特征/SubmodelConcatenation.m b/选择信道感知隐写分析(SCA)/提取特征/SubmodelConcatenation.m new file mode 100644 index 0000000..7087230 --- /dev/null +++ b/选择信道感知隐写分析(SCA)/提取特征/SubmodelConcatenation.m @@ -0,0 +1,12 @@ +function fea = SubmodelConcatenation(F) +Ss = fieldnames(F); +indexTo = 0; + for Sid = 1:length(Ss) + Fsingle = eval(['F.' Ss{Sid}]); + indexFrom = indexTo + 1; + fprintf(' F.%s : %d x %d\n', Ss{Sid}, size(Fsingle, 1), size(Fsingle, 2)); + indexTo = indexFrom + size(Fsingle,2) - 1; + fea(1,indexFrom:indexTo) = Fsingle; + end +end + diff --git a/选择信道感知隐写分析(SCA)/提取特征/maxSRM.mexw32 b/选择信道感知隐写分析(SCA)/提取特征/maxSRM.mexw32 new file mode 100644 index 0000000..38a3276 Binary files /dev/null and b/选择信道感知隐写分析(SCA)/提取特征/maxSRM.mexw32 differ diff --git a/选择信道感知隐写分析(SCA)/提取特征/maxSRM.mexw64 b/选择信道感知隐写分析(SCA)/提取特征/maxSRM.mexw64 new file mode 100644 index 0000000..b8dd59b Binary files /dev/null and b/选择信道感知隐写分析(SCA)/提取特征/maxSRM.mexw64 differ diff --git a/选择信道感知隐写分析(SCA)/集成分类器/ensemble_testing.m b/选择信道感知隐写分析(SCA)/集成分类器/ensemble_testing.m new file mode 100644 index 0000000..d0d6ef2 --- /dev/null +++ b/选择信道感知隐写分析(SCA)/集成分类器/ensemble_testing.m @@ -0,0 +1,55 @@ +function results = ensemble_testing(X,trained_ensemble) +% ------------------------------------------------------------------------- +% Ensemble Classification | June 2013 | version 2.0 | TESTING ROUTINE +% ------------------------------------------------------------------------- +% INPUT: +% - X - testing features (in a row-by-row manner) +% - trained_ensemble - trained ensemble - cell array of individual base +% learners (output of the 'ensemble_training' routine) +% OUTPUT: +% - results.predictions - individual cover (-1) and stego (+1) predictions +% based on the majority voting scheme +% - results.votes - sum of all votes (gives some information about +% prediction confidence) +% ------------------------------------------------------------------------- +% Please see the main routine 'ensemble_training' for more information. +% ------------------------------------------------------------------------- +% Copyright (c) 2013 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013 +% http://dde.binghamton.edu/download/ensemble +% ------------------------------------------------------------------------- +% References: +% [1] - J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for +% steganalysis of digital media. IEEE Transactions on Information Forensics +% and Security. Currently under review. +% ------------------------------------------------------------------------- + +% simple majority voting scheme +votes = zeros(size(X,1),1); +for i = 1:length(trained_ensemble) + proj = X(:,trained_ensemble{i}.subspace)*trained_ensemble{i}.w-trained_ensemble{i}.b; + votes = votes+sign(proj); +end + +% resolve ties randomly +votes(votes==0) = rand(sum(votes==0),1)-0.5; +% form final predictions +results.predictions = sign(votes); +% output also the sum of the individual votes (~confidence info) +results.votes = votes; diff --git a/选择信道感知隐写分析(SCA)/集成分类器/ensemble_training.m b/选择信道感知隐写分析(SCA)/集成分类器/ensemble_training.m new file mode 100644 index 0000000..78b8a82 --- /dev/null +++ b/选择信道感知隐写分析(SCA)/集成分类器/ensemble_training.m @@ -0,0 +1,609 @@ +function [trained_ensemble,results] = ensemble_training(Xc,Xs,settings) +% ------------------------------------------------------------------------- +% Ensemble Classification | June 2013 | version 2.0 +% ------------------------------------------------------------------------- +% The purpose of version 2.0 is to simplify everything as much as possible. +% Here is a list of the main modifications compared to the first version of +% the ensemble classifier: +% - Instead of a single routine, we separated training form testing. This +% allows for more flexibility in the usage. +% - Training outputs the data structure 'trained_ensemble' which allows +% for easy storing of the trained classifier. +% - Ensemble now doesn't accept paths to features any more. Instead, it +% requires the features directly (Xc - cover features, Xs - stego +% features). Xc and Xs must have the same dimension and must contain +% synchronized cover/stego pairs - see the attached tutorial for more +% details on this. +% - There is no output into a log file. So there is no hard-drive access +% at all now. +% - Since the training and testing routines were separated, our ensemble +% implementation no longer takes care of training/testing divisions. +% This is the responsibility of the user now. Again, see the attached +% tutorial for examples. +% - Bagging is now always on +% - We fixed the fclose bug (Error: too many files open) +% - Covariance caching option was removed +% - Added settings.verbose = 2 option (screen output of only the last row) +% - Ensemble now works even if full dimension is equal to 1 or 2. If equal +% to 1, multiple decisions are still combined as different base learners +% are trained on different bootstrap samples (bagging). +% ------------------------------------------------------------------------- +% Copyright (c) 2013 DDE Lab, Binghamton University, NY. +% All Rights Reserved. +% ------------------------------------------------------------------------- +% Permission to use, copy, modify, and distribute this software for +% educational, research and non-profit purposes, without fee, and without a +% written agreement is hereby granted, provided that this copyright notice +% appears in all copies. The program is supplied "as is," without any +% accompanying services from DDE Lab. DDE Lab does not warrant the +% operation of the program will be uninterrupted or error-free. The +% end-user understands that the program was developed for research purposes +% and is advised not to rely exclusively on the program for any reason. In +% no event shall Binghamton University or DDE Lab be liable to any party +% for direct, indirect, special, incidental, or consequential damages, +% including lost profits, arising out of the use of this software. DDE Lab +% disclaims any warranties, and has no obligations to provide maintenance, +% support, updates, enhancements or modifications. +% ------------------------------------------------------------------------- +% Contact: jan@kodovsky.com | fridrich@binghamton.edu | June 2013 +% http://dde.binghamton.edu/download/ensemble +% ------------------------------------------------------------------------- +% References: +% [1] - J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for +% steganalysis of digital media. IEEE Transactions on Information Forensics +% and Security. Currently under review. +% ------------------------------------------------------------------------- +% INPUT: +% Xc - cover features in a row-by-row manner +% Xs - corresponding stego features (needs to be synchronized!) +% settings +% .seed_subspaces (default = random) - PRNG seed for random subspace +% generation +% .seed_bootstrap (default = random) - PRNG seed for bootstrap samples +% generation +% .d_sub (default = 'automatic') - random subspace dimensionality; either +% an integer (e.g. 200) or the string 'automatic' is accepted; in +% the latter case, an automatic search for the optimal subspace +% dimensionality is performed, see [1] for more details +% .L (default = 'automatic') - number of random subspaces / base +% learners; either an integer (e.g. 50) or the string 'automatic' +% is accepted; in the latter case, an automatic stopping criterion +% is used, see [1] for more details +% .verbose (default = 1) - turn on/off screen output +% = 0 ... no screen output +% = 1 ... full screen output +% = 2 ... screen output of only the last row (results) +% +% Parameters for the search for d_sub (when .d_sub = 'automatic'): +% +% .k_step (default = 200) - initial step for d_sub when searching from +% left (stage 1 of Algorithm 2 in [1]) +% .Eoob_tolerance (default = 0.02) - the relative tolerance for the +% minimality of OOB within the search, i.e. specifies the stopping +% criterion for the stage 2 in Algorithm 2 +% +% Both default parameters work well for most of the steganalysis scenarios. +% +% Parameters for automatic stopping criterion for L (when .L ='automatic'); +% see [1] for more details: +% +% .L_kernel (default = ones(1,5)/5) - over how many values of OOB +% estimates is the moving average taken over +% .L_min_length (default = 25) - the minimum number of random subspaces +% that will be generated +% .L_memory (default = 50) - how many last OOB estimates need to stay in +% the epsilon tube +% .L_epsilon (default = 0.005) - specification of the epsilon tube +% +% According to our experiments, these values are sufficient for most of the +% steganalysis tasks (different algorithms and features). Nevertheless, any +% of these parameters can be modified before calling the ensemble if +% desired. +% ------------------------------------------------------------------------- +% OUTPUT: +% trained_ensemble - cell array of individual FLD base learners, each +% containing the following three fields: +% - subspace - random subspace indices +% - w - vector of weights (normal vector to the decision boundary) +% - b - bias +% results - data structure with additional results of the training +% procedure (training time, progress of the OOB error estimate, +% summary of the search for d_sub, etc. See the attached tutorial +% where we use some of these pieces of information for demonstrative +% purposes +% ------------------------------------------------------------------------- + + +if ~exist('settings','var'), settings.all_default = 1; end + +% check settings, set default values, initial screen print +[Xc,Xs,settings] = check_initial_setup(Xc,Xs,settings); + +% initialization of the search for d_sub +[SEARCH,settings,search_counter,MIN_OOB,OOB.error] = initialize_search(settings); + +% search loop (if search for d_sub is to be executed) +while SEARCH.in_progress + search_counter = search_counter+1; + + % initialization + [SEARCH.start_time_current_d_sub,i,next_random_subspace,TXT,base_learner] = deal(tic,0,1,'',cell(settings.max_number_base_learners,1)); + + % loop over individual base learners + while next_random_subspace + i = i+1; + + %%% RANDOM SUBSPACE GENERATION + base_learner{i}.subspace = generate_random_subspace(settings.randstream.subspaces,settings.max_dim,settings.d_sub); + + %%% BOOTSTRAP INITIALIZATION + OOB = bootstrap_initialization(Xc,Xs,OOB,settings); + + %%% TRAINING PHASE + base_learner{i} = FLD_training(Xc,Xs,base_learner{i},OOB,settings); + + %%% OOB ERROR ESTIMATION + OOB = update_oob_error_estimates(Xc,Xs,base_learner{i},OOB,i); + + [next_random_subspace,MSG] = getFlag_nextRandomSubspace(i,OOB,settings); + + % SCREEN OUTPUT + CT = double(toc(SEARCH.start_time_current_d_sub)); + TXT = updateTXT(TXT,sprintf(' - d_sub %s : OOB %.4f : L %i : T %.1f sec%s',k_to_string(settings.d_sub),OOB.error,i,CT,MSG),settings); + + end % while next_random_subspace + + results.search.d_sub(search_counter) = settings.d_sub; + updateLog_swipe(settings,'\n'); + + if OOB.error terminate search + SEARCH.in_progress = 0; + SEARCH.optimal_d_sub = SEARCH.x(SEARCH.E==MINIMAL_ERROR); + SEARCH.optimal_d_sub = SEARCH.optimal_d_sub(1); + return; +end + + +if minE_id == 1 + % smallest k is the best => reduce step + SEARCH.step = floor(SEARCH.step/2); + SEARCH = add_gridpoints(SEARCH,SEARCH.x(1)+SEARCH.step*[-1 1]); +elseif minE_id == length(SEARCH.x) + % largest k is the best + if SEARCH.x(end) + SEARCH.step <= settings.max_dim && (min(abs(SEARCH.x(end) + SEARCH.step-SEARCH.x))>SEARCH.step/2) + % continue to the right + SEARCH = add_gridpoints(SEARCH,SEARCH.x(end) + SEARCH.step); + else + % hitting the full dimensionality + if (MINIMAL_ERROR/SEARCH.E(end-1) >= 1 - settings.Eoob_tolerance) ... % desired tolerance fulfilled + || SEARCH.E(end-1)-MINIMAL_ERROR < 5e-3 ... % maximal precision in terms of error set to 0.5% + || SEARCH.stepSEARCH.step/2) ... % one more step to the right is not too close to any other point + && ~(SEARCH.E(end)>SEARCH.E(end-1) && SEARCH.E(end)>SEARCH.E(end-2)) % the last point is not worse than the two previous ones + % robustness ensurance, try one more step to the right + SEARCH = add_gridpoints(SEARCH,settings.d_sub + SEARCH.step); +else + % best k is not at the edge of the grid (and robustness is resolved) + err_around = mean(SEARCH.E(minE_id+[-1 1])); + if (MINIMAL_ERROR/err_around >= 1 - settings.Eoob_tolerance) ... % desired tolerance fulfilled + || err_around-MINIMAL_ERROR < 5e-3 ... % maximal precision in terms of error set to 0.5% + || SEARCH.step= settings.max_dim/4, settings.d_sub_step = floor(settings.max_dim/4); end + if settings.max_dim < 10, settings.d_sub_step = 1; end + SEARCH.x = settings.d_sub_step*[1 2 3]; + if settings.max_dim==2, SEARCH.x = [1 2]; end + SEARCH.E = -ones(size(SEARCH.x)); + SEARCH.terminate = 0; + SEARCH.step = settings.d_sub_step; + settings.d_sub = SEARCH.x(1); +end + +search_counter = 0; +MIN_OOB = 1; +OOB_error = 1; + +function TXT = updateTXT(old,TXT,settings) +if isfield(settings,'kmin') + if length(TXT)>3 + if ~strcmp(TXT(1:3),' - ') + TXT = [' - ' TXT]; + end + end +end +if settings.verbose==1 + if exist('/home','dir') + % do not delete on cluster, it displays incorrectly when writing through STDOUT into file + fprintf(['\n' TXT]); + else + fprintf([repmat('\b',1,length(old)) TXT]); + end +end + +function s = k_to_string(k) +if length(k)==1 + s = num2str(k); + return; +end + +s=['[' num2str(k(1))]; +for i=2:length(k) + s = [s ',' num2str(k(i))]; %#ok +end +s = [s ']']; + +function updateLog_swipe(settings,TXT,final) +if ~exist('final','var'), final=0; end +if settings.verbose==1 || (settings.verbose==2 && final==1), fprintf(TXT); end + +function OOB = bootstrap_initialization(Xc,Xs,OOB,settings) +% initialization of the structure for OOB error estimates +OOB.SUB = floor(size(Xc,1)*rand(settings.randstream.bootstrap,size(Xc,1),1))+1; +OOB.ID = setdiff(1:size(Xc,1),OOB.SUB); +if ~isfield(OOB,'Xc') + OOB.Xc.fusion_majority_vote = zeros(size(Xc,1),1); % majority voting fusion + OOB.Xc.num = zeros(size(Xc,1),1); % number of fused votes + OOB.Xs.fusion_majority_vote = zeros(size(Xs,1),1); % majority voting fusion + OOB.Xs.num = zeros(size(Xs,1),1); % number of fused votes +end +if ~isfield(OOB,'randstream_for_ties') + % Doesn't really matter that we fix the seed here. This will be used + % only for resolving voting ties. We are fixing this in order to make + % all results nicely reproducible. + OOB.randstream_for_ties = RandStream('mt19937ar','Seed',1); +end + + +function [base_learner] = findThreshold(Xm,Xp,base_learner) +% find threshold through minimizing (MD+FA)/2, where MD stands for the +% missed detection rate and FA for the false alarms rate +P1 = Xm*base_learner.w + sqrt(1)*randn(size(Xm,1),1); +P2 = Xp*base_learner.w + sqrt(1)*randn(size(Xm,1),1); +L = [-ones(size(Xm,1),1);ones(size(Xp,1),1)]; +[P,IX] = sort([P1;P2]); +L = L(IX); +Lm = (L==-1); +sgn = 1; + +MD = 0; +FA = sum(Lm); +MD2=FA; +FA2=MD; +Emin = (FA+MD); +Eact = zeros(size(L-1)); +Eact2 = Eact; +for idTr=1:length(P)-1 + if L(idTr)==-1 + FA=FA-1; + MD2=MD2+1; + else + FA2=FA2-1; + MD=MD+1; + end + Eact(idTr) = FA+MD; + Eact2(idTr) = FA2+MD2; + if Eact(idTr)0)+sum(TMP_s<0))/(length(TMP_c)+length(TMP_s)); + +if ~ischar(OOB) && ~isempty(OOB) + H = hist([OOB.Xc.num;OOB.Xs.num],0:max([OOB.Xc.num;OOB.Xs.num])); + avg_L = sum(H.*(0:length(H)-1))/sum(H); % average L in OOB + OOB.x(i) = avg_L; + OOB.y(i) = OOB.error; +end + +function base_learner = FLD_training(Xc,Xs,base_learner,OOB,settings) +% FLD TRAINING +Xm = Xc(OOB.SUB,base_learner.subspace); +Xp = Xs(OOB.SUB,base_learner.subspace); + +% remove constants +remove = false(1,size(Xm,2)); +adepts = unique([find(Xm(1,:)==Xm(2,:)) find(Xp(1,:)==Xp(2,:))]); +for ad_id = adepts + U1=unique(Xm(:,ad_id)); + if numel(U1)==1 + U2=unique(Xp(:,ad_id)); + if numel(U2)==1, if U1==U2, remove(ad_id) = true; end; end + end +end + +muC = sum(Xm,1); muC = double(muC)/size(Xm,1); +muS = sum(Xp,1); muS = double(muS)/size(Xp,1); +mu = (muS-muC)'; + +% calculate sigC +xc = bsxfun(@minus,Xm,muC); +sigC = xc'*xc; +sigC = double(sigC)/size(Xm,1); + +% calculate sigS +xc = bsxfun(@minus,Xp,muS); +sigS = xc'*xc; +sigS = double(sigS)/size(Xp,1); + +sigCS = sigC + sigS; + +% regularization +sigCS = sigCS + 1e-10*eye(size(sigC,1)); + +% check for NaN values (may occur when the feature value is constant over images) +nan_values = sum(isnan(sigCS))>0; +nan_values = nan_values | remove; + +sigCS = sigCS(~nan_values,~nan_values); +mu = mu(~nan_values); +lastwarn(''); +warning('off','MATLAB:nearlySingularMatrix'); +warning('off','MATLAB:singularMatrix'); +base_learner.w = sigCS\mu; +% regularization (if necessary) +[txt,warnid] = lastwarn(); %#ok +while strcmp(warnid,'MATLAB:singularMatrix') || (strcmp(warnid,'MATLAB:nearlySingularMatrix') && ~settings.ignore_nearly_singular_matrix_warning) + lastwarn(''); + if ~exist('counter','var'), counter=1; else counter = counter*5; end + sigCS = sigCS + counter*eps*eye(size(sigCS,1)); + base_learner.w = sigCS\mu; + [txt,warnid] = lastwarn(); %#ok +end +warning('on','MATLAB:nearlySingularMatrix'); +warning('on','MATLAB:singularMatrix'); +if length(sigCS)~=length(sigC) + % resolve previously found NaN values, set the corresponding elements of w equal to zero + w_new = zeros(length(sigC),1); + w_new(~nan_values) = base_learner.w; + base_learner.w = w_new; +end + +% find threshold to minimize FA+MD +[base_learner] = findThreshold(Xm,Xp,base_learner); + +function results = add_search_info(results,settings,search_counter,SEARCH,i,CT) +% update information about d_sub search +if settings.search_for_d_sub + results.search.OOB(search_counter) = SEARCH.E(SEARCH.x==results.search.d_sub(search_counter)); + results.search.L(search_counter) = i; + results.search.time(search_counter) = CT; +end + +function SEARCH = add_gridpoints(SEARCH,points) +% add new points for the search for d_sub +for point=points + if SEARCH.x(1)>point + SEARCH.x = [point SEARCH.x]; + SEARCH.E = [-1 SEARCH.E]; + continue; + end + if SEARCH.x(end)