信息隐藏实验五-stirmark与jsteg全解
隐马尔可夫模型详解ppt(有例子_具体易懂)
例（续）
如果第一天为晴天，根据这一模型，在今后七天中天 气为O=“晴晴雨雨晴云晴”的概率为：
隐马尔可夫模型 （Hidden Markov Model, HMM）
在MM中，每一个状态代表一个可观察的 事件 在HMM中观察到的事件是状态的随机函数， 因此该模型是一双重随机过程，其中状态 转移过程是不可观察（隐蔽）的(马尔可夫 链)，而可观察的事件的随机过程是隐蔽的 状态转换过程的随机函数(一般随机过程)。
递归专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料前向算法过程演示int1t2t3t4t5ttt6t7tt1in1i5i4i3i2i1专业资料前向算法过程演示t1t2t3t4t5ttt6t7tt1inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt6t7tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5ttt7t6tt1前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5tttt1t6t7前向算法过程演示inin1i5i4i3i2i1专业资料t1t2t3t4t5tttt1t6t7前向算法过程演示inin1i5i4i3i2i1专业资料前向算法过程演示t1t2t3t4t5tttt1t6t7inin1i5i4i3i2i13
浅谈心理测试技术中的隐蔽信息测试法(CIT)
关键词： ＬＦ ｉ 试 技 术 隐 敝信息 ｊ 波 Ｈ标 越 ，． ｌ ，ｔ￣ Ｊ！ ！ｌ ｊ ｝ ｊ ｌ ｌ ｊ来自ｌ 涪衬问题 一、
引 言
案关键信息与背景信息上的认知加工差异 、生理反应差异 做出分析，并对被测试人与案件相关或无关的心理信息做 出验证性或探索性的检测 ，从而为测试结论提供最直接的
激活之后 ，已有心理信息会 凸显于背 景信息之上 ，随即形
ＣＴ ｌ测试主要涉及到三类基本 的问题形式 ，即目标 问
题 （ 或称为关键问题 、相关问题、靶问题等 ） 、陪衬 问题 （ 称 为背 景 问题 ）以及 无 关 问题 （ 称 为非 相关 问 或 或 题 ） 另外 ，还可以在实际操作中根据测试类型与测试取 。
加 工 的 重 点 信 息 ， 并 在 记 忆 提 取 过 程 中 出现 优 势 加 工 效
原 理与 紧张峰测试 相同 ，现 代心理测试 技术 已将 这两类
测 试 统 一 于ＣＩ的 体 系 之 下 。 Ｔ ２ 、ＣＩ测 试 的题 目组 织 形 式 Ｔ
应 。在这一原理 指导之下 ，ＣＩ测试将 明确 的或潜 在的涉 Ｔ 案情节作为关键性的心理刺激 ，同时设置具有 比对功能的 背景刺激。 当作案者记忆 中与真实案情高度相关的信息被
心 理 测 试 技 术 是 在 侦 查 理 论 研 究 与 实践 过 程 中 不 断
形成与发展起来的一 门应用性很强的技术 ，该技术在早期 主要通过 系统的测试题 目和专业测试仪器 ，捕捉犯罪者说 谎时特异 的生理反应指标 ，进而对其是否说谎做 出推断。 现代心理测试技术已不仅仅关注 “ 欺骗性表述特征及其反 应模式” ，更多的是要试图探查与分析被测试人的心理信 息”，并在 实践 中基于对犯罪现场关键信息 的分析来设 计 题 目，借助现代认知心理学原理来查明犯罪者作案的心理 痕迹。通过对心理信息 的探测 ，在认知层面上实现犯罪行 为的重建 ，为侦查取证工作提供科学参考。 从测试过程来看 ，心理测试 的技术体 系主要由测 试
lsb信息隐藏
LSB算法的信息隐藏实验单位：三系一队姓名：马波学号：3222008030LSB信息隐藏实验一、实验目的1.掌握LSB算法原理2.熟悉信息隐藏与提取的流程3.锻炼算法的程序实现能力二、实验原理1.信息隐藏用秘密信息比特替换载体中的最不重要部分，可以达到对信息隐藏的目的。
在数字图像中，每个字节的最低位对图像信息的影响最小，因此将数字图像的最低位用信息比特替换可以实现信息隐藏。
由于载体图像的每个字节只隐藏一个秘密信息比特，所以只有当载体图像的大小是秘密信息大小的8倍以上时才能完整的将秘密信息隐藏。
提取信息位并隐藏的示意图：2.信息提取在隐藏了秘密信息的数字图像中，每个字节的最低位就是秘密信息比特位，只需将这些信息比特提取出来并组合，就可以恢复出原来的秘密信息。
提取信息示意图：三、实验内容A.将秘密信息隐藏在载体的最低位，检验算法的鲁棒性（1）读入秘密信息(此实验中秘密信息为二值图像)（2）把秘密信息的比特位放入载体的最低位（3）给隐藏了秘密信息的图像加入大小为1的噪声加入噪声大小为1时：加入噪声为2时：B．将秘密信息隐藏在载体的最高位，检验算法的鲁棒性（1）读入秘密信息(此实验中秘密信息为二值图像)（2）把秘密信息的比特位隐藏在载体的最高位（3）分别给隐藏了秘密信息的图像加入大小为1和2的噪声C．将秘密信息隐藏在载体的第三位，检验算法的鲁棒性（1）同A中的（1）（2）把秘密信息比特位隐藏在载体的第三位（3）分别给隐藏了秘密信息的图片加入大小为1、2和3的噪声五、实验总结1.当秘密信息隐藏在最低位时，对载体的改变小，载体质量较高。
但鲁棒性较差，有噪声干扰时很容易发生信息丢失从而无法恢复出秘密信息2.当秘密信息隐藏在最高位时，图像的鲁棒性增强，受到较大噪声干扰时仍能恢复出秘密信息，但对图像的改变较大，隐藏的位数越高图像的质量越低。
3.当隐藏的信息位介于最低位和最高位时，选择合适的位置，既可以提高信息隐藏的鲁棒性，又对图像的质量影响不大，所以，进行信息隐藏时可以考虑LSB的改进。
基于DCT的JSteg隐写及分析
基于DCT的JSteg隐写及分析一、摘要 (1)二、引言 (3)三、JSteg隐写 (4)3.1 JSteg简介 (4)3.2 JSteg算法 (5)3.3 JSteg隐写过程 (6)四、JSteg隐写检测 (7)4．1基于小波特征函数统计矩的隐写分析··74.2基于支持向量机的多特征盲检测算法 (9)五、总结 (10)【参考文献】 (11)附录 (12)JSteg隐写代码（matlab） (12)一、摘要JPEG是互联网上最为常见的一种图像格式，而DCT变换是JPEG压缩采用的重要技术之一，在DCT变换系数（DCT域）上隐藏信息是常见的数字隐写方式。
DCT（Discrete Cosine Transform，离散余弦变换）是一种实数域变换，其变换核为实数余弦函数。
作为DCT变换的方法之一，JSteg是一种采用JPEG图像作为载体的隐写软件，其算法实际上就是将空域LSB替换隐写应用到JPEG图像上。
主要思想是：将一个二进制位的隐秘信息嵌入到量化后的DCT系数的LSB上，但对原始值为．1、0、1的DCT系数例外，提取隐秘信息时，只需将载密图像中不等于．1、O、l的量化DCT系数的LSB 逐一取出即可。
JSteg算法虽然简单易用，但由于其会引起系数直方图出现值对区域相等的特点，用卡方分析可以很容易的检测到秘密信息的存在，因此其安全性较差。
关键词：JPEG DCT JSteg 实数余弦函数Based on the analysis of DCTsteganographyAbstractJPEG is the Internet's most common image format, JPEG compression and the DCT is one of the key technology used in the DCT coefficients (DCT domain) on the hidden information is a common digital steganography way. DCT (Discrete Cosine Transform, Discrete Cosine Transform) is a real domain transform, which is a real number cosine transform kernel functions. As one method of DCT, JSteg aJPEG image using steganography software as a carrier, the algorithm will actually replace airspace LSB steganography applied to JPEG images. The main idea is: to embed a bit of secret information to the LSB of quantized DCT coefficients, but the original value. 1,0,1 DCT coefficients exception when extracting hidden information, simply stego image is not equal. 1, LSB O, l quantized DCT coefficients can be taken out one by one. JSteg algorithm is simple and easy to use, but because it will cause the value of the coefficient equal to the regional characteristics of the histogram appears chi-square analysis can easily detect the presence of secret information, so the security is poor.Keywords: JPEG DCT JSteg real cosine function二、引言作为信息安全的分支，隐写技术主要是针对图片等外在特征较为明显的载体写入想要隐藏的信息，用以达到信息隐藏的目的。
信息隐藏实验(LSB隐写,随机LSB隐写,RS隐写分析)
信息隐藏实验二LSB隐写分析姓名：周伟康学号：班级：一：实验要求1、针对自己实现的隐写算法（嵌入、提取），计算隐蔽载体的PSNR值，通过PSNR值来评估隐写对图像质量的影响，并与主观感受做对比。
2、实现一种隐写分析方法，对隐蔽载体进行检测（卡方、RS……）二：实验步骤1、编写随机选点函数，完善顺序和随机两种LSB信息嵌入和提取。
%随机间隔选点函数%[row, col] = randinterval(test, 60, 1983);function [row, col] = randinterval(matrix, count, key)[m, n] = size(matrix);interval1 = floor(m * n / count) + 1;interval2 = interval1 - 2;if interval2 == 0error('载体太小，不能将秘密消息隐藏其内！');endrand('seed', key);a = rand(1, count);%initializerow = zeros([1 count]);col = zeros([1 count]);r = 1; c = 1;row(1,1) = r;col(1,1) = c;for i = 2 : countif a(i) >= 0.5c = c + interval1;elsec = c + interval2;endif c > nr = r + 1;if r > merror('载体太小，不能将秘密消息隐藏其内！');endc = mod(c, n);if c==0c = 1;endendrow(1, i) = r;col(1, i) = c;end选取8*8的矩阵测试2、对比原始图像和隐藏信息后图像，计算隐蔽载体的均方差（MSE）进而计算峰值信噪比（PSNR），评估隐写对图像质量的影响。
【硕士论文】图像中隐形信息的检测
图像中隐形信息的检测ｔ海师范大学硕士学位论文第一章绪论１．１引言随着Ｉｎｔｅｍｅｔ的普及，各种数字媒体技术的成熟，网络通信越来越显出其方便快捷的优越性。
现代科学技术正在悄悄改变人们的生活习惯。
越来越多的人开始利用网络来发布信息、接收信件、下载资料。
与此同时，现代数字隐写技术也在飞速发展。
人们可以利用软件对图片、声音、视频、文本等数字媒体进行修改，在文件中加入自己的数据，且保持原文件的大小及外观。
图１．１和图１．２显示了一幅原始图像和一幅经过ＪＳｔｅｇ算法隐写的含密图像，我们无法用肉眼发现它们的差别。
图１．１原始图像图１．２含密图像自古已来，中国就有了“耳闻为虚，眼见为实”，“百闻不如一见”等谚语，由上图可以说“眼见不一定为实”。
人们过于相信自己的眼睛，使得信息隐藏技术有了存在和发展的空间，但随之而来的是严重的安全问题。
据报道，本．拉登的基地组织就利用了数字隐写技术在因特网上传递消息。
我们知道在因特网上传播的文件是海量的。
同时只要控制嵌入的数据量，经过隐写的含密文件和原始文件将非常相似，几乎无法用肉眼来分辩。
如何才能快速有效的对大量不同格式的数字文件进行检测？数字隐写分析术应运而生。
我们知道，只要在文件中嵌入信息，就会对文件进行修改，从而改变文件的某些特征。
数字隐写分析术就是利用数学工具，找到被改变的特征，将这种变化放大，以此来对原始文件和含密文件进行分类，达到检测的目的。
（３）该方法只衡量了原始图像和含密图像的低阶统计特性，没有考虑含密图像的实际失真度。
丽实际可能会存在两者的相关熵很小，但是失真很大，此时也是不安全的。
２．４数字隐写算法的分类基于图像的数字隐写算法，可以分成基于时空域的隐写算法和基于变换域的隐写算法【３４１，ｆ３５】。
时空域隐写技术直接针对像素，一般先把数据分解成Ｏ和ｌ的位序列，然后用它来替换像素的ＬＳＢ位。
我们往往可以把嚎素的ＬＳＢ位看成随机的噪声，它对人眼的视觉影响不大。
图２．３分别将原始图像的ＬＳＢ位置全１和全０，我们无法用肉眼分辩两者的差别。
信息隐藏实验报告-信息隐藏技术
实验目的隐写分析以及变换域隐写技术实验内容针对LSB隐写的卡方分析a)实现针对LSB隐写的卡方分析b)分析实验性能针对LSB隐写的RS分析a)实现针对LSB隐写的RS分析b)分析实验性能JPEG压缩算法a)分析JPEG压缩算法的主要流程Jsteg隐写算法a)实现Jsteg隐写算法b)分析实验性能F3隐写算法a)实现F3隐写算法b)分析实验性能实验工具及平台■Windows+Matlab□其它：（请注明）实验涉及到的相关算法1、与实验内容选择的项目对应；2、请使用流程图、伪代码、NS 图或文字方式描述，不要．．贴代码 卡方隐写分析卡方隐写分析主要利用了LSB 隐写后图像的值对效应。
它需要LSB 隐写满足如下的条件：1. 嵌入信息中0、1的分布较为均匀，即各为50%左右。
由于信息嵌入到载体之前通常需要经过加密操作，因此这一点是容易满足的。
2. 图像需要有较多的像素点被嵌入信息。
当嵌入信息较少时，卡方分析的效果并不精确。
卡方分析的原理是：若设ℎj 表示图像载体中灰度值为j 的像素数量，如果载体图像没有使用LSB 隐写算法，那么ℎ2i 和ℎ2i+1的值通常相差较大，而LSB 隐写方法将秘密信息取代图像的最低位，由于秘密信息通常是加密过的，因此可以看成0、1分布均匀的比特流。
在嵌入过程中只存在2i →2i +1而不存在2i →2i −1的变换，因此使得ℎ2i 和ℎ2i+1的值趋于一致，我们能够借助改变的统计特性判断图像是否经过隐写。
我们首先定义ℎ2i ∗=ℎ2i +ℎ2i+12，由LSB 隐写算法的性质我们可以知道在嵌入前后该值是不变的。
由中心极限定理，我们有ℎ2i −ℎ2i+1√(2ℎ2i ∗)→N(0,1) 因此r = ∑(ℎ2i −ℎ2i ∗)2ℎ2i ∗k i=1服从卡方分布。
结合卡方分布的密度计算函数我们可以计算出载体被隐写的可能性为：p =1−12k−12T(k −12)∫exp (−t 2)t k−12−1dt r0 当p 的值接近于1时，我们可以推断出载体图像中含有秘密信息。
【毕业论文】恶意代码分析实例
【毕业论文】恶意代码分析实例恶意代码实例分析2011 年 5 月1目录1虚拟环境及所用软件介绍 (1)1.1虚拟环境介绍 ..................................................................... (1)1.1.1 Vmware Workstation7.1.4 .................................................................. (1)1.1.2 Gost XP SP3 装机版YN9.9................................................................... .. 11.2 检查软件介绍 ..................................................................... .. (1)1.2.1 ATool1.0.1.0 ................................................................ .. (1)1.2.2 Regmon 7.04 汉化版...................................................................... . (1)1.2.3 FileMon 7.04 汉化版 ..................................................................... .. (2)1.2.4 TCPView3.04.................................................................... .. (2)1.2.5procexp.exe ............................................................ . (2)1.2.6 IceSword 1.22 中文版 ..................................................................... (2)2 木马冰河分析与检测 ......................................................................32.1 木马冰河V2.2介绍 ..................................................................... ....................... 3 2.2 样本分析 ..................................................................... (3)2.2.1 进程监测 ..................................................................... .. (3)2.2.2 文件监测 ..................................................................... .. (3)2.2.3 注册表监测...................................................................... . (4)2.2.4系统通信端口监测 ..................................................................... ................ 5 2.3 样本外部特征总结 ..................................................................... ......................... 5 2.4 木马清除方法 ..................................................................... .. (5)3 xueranwyt.exe木马分析与监测 (7)3.1 木马xueranwyt.exe介绍...................................................................... .............. 7 3.2 样本分析 ..................................................................... (7)3.2.1进程监测 ..................................................................... (7)3.2.2 文件监测 ..................................................................... .. (7)3.2.3 注册表监控...................................................................... . (8)3.2.4 端口监测 ..................................................................... ............................. 8 3.3 样本外部特征总结 ..................................................................... ......................... 8 3.4 解决方案 ..................................................................... (9)4 2.exe木马分析与监测 (10)4.1 木马样本2.exe介绍...................................................................... ................... 10 4.2 样本分析 ..................................................................... . (10)4.2.1 进程监控 ..................................................................... (10)4.2.2 文件监控 ..................................................................... (10)4.2.3 注册表监控...................................................................... .. (11)4.2.4 端口检测 ..................................................................... ........................... 11 4.3 样本外部特征总结 ..................................................................... ....................... 12 4.4 解决方案 ..................................................................... . (12)25 红蜘蛛样本分析与检测 (13)5.1 样本介绍 ..................................................................... ..................................... 13 5.2 样本分析 ..................................................................... . (13)5.2.1 进程检测 ..................................................................... (13)5.2.2 文件检测 ..................................................................... (13)5.2.3 注册表监控...................................................................... .. (14)5.2.4 端口监控 ..................................................................... ........................... 14 5.3 样本外部特征总结 ..................................................................... ....................... 14 5.4 解决方案 ..................................................................... . (15)6 031gangsir.ch.exe样本分析 (16)6.1 样本介绍 ..................................................................... ..................................... 16 6.2 样本分析 ..................................................................... . (16)6.2.1 进程监控 ..................................................................... (16)6.2.2 文件监控 ..................................................................... (16)6.2.3 注册表监控...................................................................... .. (17)6.2.4 端口监控 ..................................................................... ........................... 17 6.3 样本特征总结 ..................................................................... .............................. 17 6.4 解决方案 ..................................................................... . (18)7 .exe样本监测与分析 (19)7.1 样本简介 ..................................................................... ..................................... 19 7.2 样本分析 ..................................................................... . (19)7.2.1 进程监控 ..................................................................... (19)7.2.2 文件监控 ..................................................................... (19)7.2.3 注册表监控...................................................................... .. (20)7.2.4 端口监控 ..................................................................... ........................... 20 7.3 样本外部特征总结 ..................................................................... ....................... 20 7.4 解决方案 ..................................................................... . (21)8 .exe样本监测与分析 (22)8.1 样本信息介绍 ..................................................................... .............................. 22 8.2 样本分析 ..................................................................... . (22)8.2.1进程监控 ..................................................................... . (22)8.2.2 文件监控 ..................................................................... (22)8.2.3 注册表监控...................................................................... .. (22)8.2.4 端口监控 ..................................................................... ........................... 23 8.3 样本外部特征总结 ..................................................................... ....................... 23 8.4解决方案 ..................................................................... .. (24)9 .exe样本分析与监测 (25)9.1 样本简介 ..................................................................... ..................................... 25 9.2 样本分析 ..................................................................... . (25)39.2.1 进程监控 ..................................................................... (25)9.2.2 文件监控 ..................................................................... (25)9.2.3 注册表监控...................................................................... .. (26)9.2.4 端口监控 ..................................................................... ........................... 26 9.3 样本外部特征总结 ..................................................................... ....................... 26 9.4 解决方案 ..................................................................... . (26)10 ................................................................. (27)10.1 样本简介 ..................................................................... ................................... 27 10.2 样本分析 ..................................................................... .. (27)10.2.1 进程监控 ..................................................................... . (27)10.2.2 文件监控 ..................................................................... . (27)10.2.3 注册表监控...................................................................... (28)10.2.4 端口监控 ..................................................................... ......................... 28 10.3 样本外部特征总结 ..................................................................... ..................... 28 10.4 解决方案 ..................................................................... .. (29)11 NetThief12.9样本分析与检测 (30)11.1 样本简介 ..................................................................... ................................... 30 11.2 样本分析 ..................................................................... .. (30)11.2.1 进程监控 ..................................................................... . (30)11.2.2 文件监控 ..................................................................... . (30)11.2.3 注册表监控 ..................................................................... . (30)11.2.4 端口监控 ..................................................................... ......................... 31 11.3 样本外部特征总结 ..................................................................... ..................... 31 11.4 解决方案 ..................................................................... .. (31)41虚拟环境及所用软件介绍1.1虚拟环境介绍1.1.1 Vmware Workstation 7.1.4恶意代码具有很强的破坏性和传播性，为了系统的安全，所以实例的分析均在虚拟机下进行。
信息隐藏 实验五 Patchwork 图像信息隐藏
实验五 Patchwork 图像信息隐藏一，实验目的1，了解Patchwork信息隐藏特点，2，掌握基于Patchwork 的图像信息隐藏原理3，设计并实现一种 Patchwork 的信息隐藏方法二，实验环境1， Windows XP 操作系统2， Matlab 7.1版本软件3， BMP格式图片文件三，实验原理1，Patchwork是指从载体数据中选择一些数据组成两个集合，通过修改这两个集合之间的某种关系来携带水印信息。
这两个集合可以是两个系数、两组系数或者是两个特征量。
两个集合之间的关系可以是大小关系、能量关系、逻辑关系和奇偶关系等。
Patchwork方法嵌入水印时，通过修改集合之间的某种关系来嵌入水印；提取水印时则根据对应的关系来提取嵌入的水印信息。
2，在本实验报告中，验证了通过随机方式把像素分组的方法。
随机选择N对像素点（ai和bi），然后将ai点的值增加d，将bi点的像素值减少d。
3，同时，设计了自己的算法。
先把图像的像素写成一维矩阵，根据矩阵下标4*n形式和4*n-1形式分为两组，将下标为4*n形式所对应的像素增加常量d=2.3，将下标为4*n-1形式所对应的像素减少常量d=2.3。
四，实验内容1, 验证通过随机方式把像素分组的方法。
(1)嵌入秘密信息clc;clear all;oi=imread('baboon.bmp');%读入载体图像ni=rgb2gray(oi);wi=ni;[row col]=size(wi);wi=double(wi);wi=wi(:);n=floor((row*col)/10);length=row*col;rand('state',123);%产生随机数的密钥a=rand(1,n);%产生N长度的随机数d=2.3;%定义修改的分量count=0;k=1;while k<=nif (a(1,k)>=0.5)wi(k*10,1)=wi(k*10,1)+d;wi(k*10-1,1)=wi(k*10-1,1)-d; endk=k+1;endfor i=1:rowfor j=1:colwil(i,j)=wi(row*(j-1)+i,1);endendwil=uint8(wil);imwrite(wil,'watermarked.bmp'); subplot(1,2,1);imshow(ni);%显示原始图像subplot(1,2,2);imshow(wil)%显示新图像下图为原图与嵌入信息的图像：（2）计算两个样本均值的差clc;clear;oi=imread('watermarked.bmp');%读入嵌入水印后的图像wi=oi;[row col]=size(wi);wi=double(wi);wi=wi(:);n=floor((row*col)/10);r=1.6;rand('state',123);%产生随机数的密钥a=rand(1,n);%产生N长度的随机数d=2.3;%定义修改的分量count=0;k=1;tempa=0;tempb=0;while k<=nif(a(1,k)>=0.5)tempa=tempa+wi(k*10,1);tempb=tempb+wi(k*10-1,1);count=count+1;endk=k+1;endavea=tempa/count;aveb=tempb/count;if((avea-aveb)>r*d)watermark=1;elsewatermark=0;end计算后的结果在workspace中可以看出：2，设计了自己的算法。
基于DCT的JSteg隐写及分析
基于DCT的JSteg隐写及分析一、摘要 (1)二、引言 (3)三、JSteg隐写 (4)3.1 JSteg简介 (4)3.2 JSteg算法 (4)3.3 JSteg隐写过程 (6)四、JSteg隐写检测 (7)4．1基于小波特征函数统计矩的隐写分析 (7)4.2基于支持向量机的多特征盲检测算法 (9)五、总结 (10)【参考文献】 (11)附录 (12)JSteg隐写代码（matlab） (12)一、摘要JPEG是互联网上最为常见的一种图像格式，而DCT变换是JPEG 压缩采用的重要技术之一，在DCT变换系数（DCT域）上隐藏信息是常见的数字隐写方式。
DCT（Discrete Cosine Transform，离散余弦变换）是一种实数域变换，其变换核为实数余弦函数。
作为DCT变换的方法之一，JSteg是一种采用JPEG图像作为载体的隐写软件，其算法实际上就是将空域LSB替换隐写应用到JPEG图像上。
主要思想是：将一个二进制位的隐秘信息嵌入到量化后的DCT系数的LSB上，但对原始值为．1、0、1的DCT系数例外，提取隐秘信息时，只需将载密图像中不等于．1、O、l的量化DCT系数的LSB逐一取出即可。
JSteg算法虽然简单易用，但由于其会引起系数直方图出现值对区域相等的特点，用卡方分析可以很容易的检测到秘密信息的存在，因此其安全性较差。
关键词：JPEG DCT JSteg 实数余弦函数Based on the analysis of DCTsteganographyAbstractJPEG is the Internet's most common image format, JPEG compression and the DCT is one of the key technology used in the DCT coefficients (DCT domain) on the hidden information is a common digital steganography way. DCT (Discrete Cosine Transform, Discrete Cosine Transform) is a real domain transform, which is a real number cosine transform kernel functions. As one method of DCT, JSteg a JPEG image using steganography software as a carrier, the algorithm will actually replace airspace LSB steganography applied to JPEGimages. The main idea is: to embed a bit of secret information to the LSB of quantized DCT coefficients, but the original value. 1,0,1 DCT coefficients exception when extracting hidden information, simply stego image is not equal. 1, LSB O, l quantized DCT coefficients can be taken out one by one. JSteg algorithm is simple and easy to use, but because it will cause the value of the coefficient equal to the regional characteristics of the histogram appears chi-square analysis can easily detect the presence of secret information, so the security is poor.Keywords: JPEG DCT JSteg real cosine function 二、引言作为信息安全的分支，隐写技术主要是针对图片等外在特征较为明显的载体写入想要隐藏的信息，用以达到信息隐藏的目的。
信息隐藏实验报告总结(3篇)
第1篇一、实验背景随着信息技术的飞速发展，信息安全问题日益突出。
信息隐藏技术作为一种隐蔽通信手段，在军事、商业、医疗等多个领域具有重要的应用价值。
本实验旨在通过实际操作，深入了解信息隐藏技术的基本原理，掌握其实现方法，并分析其在实际应用中的优缺点。
二、实验目的1. 理解信息隐藏技术的概念、原理和应用领域。
2. 掌握信息隐藏技术的实现方法，包括空域、频域和变换域等方法。
3. 分析信息隐藏技术的安全性、鲁棒性和可检测性。
4. 结合实际案例，探讨信息隐藏技术在各个领域的应用。
三、实验内容本次实验主要分为以下几个部分：1. 信息隐藏技术概述：介绍了信息隐藏技术的概念、原理和应用领域，并简要分析了信息隐藏技术的安全性、鲁棒性和可检测性。
2. 空域信息隐藏：通过将秘密信息嵌入到载体图像的像素值中，实现信息的隐蔽传输。
实验中，我们采用了基于直方图平移的算法，将秘密信息嵌入到载体图像中。
3. 频域信息隐藏：将秘密信息嵌入到载体图像的频域系数中，实现信息的隐蔽传输。
实验中，我们采用了基于DCT变换的算法，将秘密信息嵌入到载体图像的DCT系数中。
4. 变换域信息隐藏：将秘密信息嵌入到载体图像的变换域系数中，实现信息的隐蔽传输。
实验中，我们采用了基于小波变换的算法，将秘密信息嵌入到载体图像的小波系数中。
5. 信息隐藏技术的安全性、鲁棒性和可检测性分析：通过实验，分析了不同信息隐藏方法的优缺点，并探讨了如何提高信息隐藏技术的安全性、鲁棒性和可检测性。
6. 信息隐藏技术在各个领域的应用：结合实际案例，探讨了信息隐藏技术在军事、商业、医疗等领域的应用。
四、实验结果与分析1. 空域信息隐藏：实验结果表明，基于直方图平移的算法能够将秘密信息嵌入到载体图像中，且嵌入过程对图像质量的影响较小。
然而，该方法对噪声和压缩等攻击较为敏感。
2. 频域信息隐藏：实验结果表明，基于DCT变换的算法能够将秘密信息嵌入到载体图像的频域系数中，且嵌入过程对图像质量的影响较小。
如何用简单易懂的例子解释隐马尔可夫模型
如何用简单易懂的例子解释隐马尔可夫模型？- 知乎隐马尔可夫（HMM）好讲，简单易懂不好讲。
我想说个更通俗易懂的例子。
我希望我的读者是对这个问题感兴趣的入门者，所以我会多阐述数学思想，少写公式。
霍金曾经说过，你多写一个公式，就会少一半的读者。
还是用最经典的例子，掷骰子。
假设我手里有三个不同的骰子。
第一个骰子是我们平常见的骰子（称这个骰子为D6），6个面，每个面（1，2，3，4，5，6）出现的概率是1/6。
第二个骰子是个四面体（称这个骰子为D4），每个面（1，2，3，4）出现的概率是1/4。
第三个骰子有八个面（称这个骰子为D8），每个面（1，2，3，4，5，6，7，8）出现的概率是1/8。
假设我们开始掷骰子，我们先从三个骰子里挑一个，挑到每一个骰子的概率都是1/3。
然后我们掷骰子，得到一个数字，1，2，3，4，5，6，7，8中的一个。
不停的重复上述过程，我们会得到一串数字，每个数字都是1，2，3，4，5，6，7，8中的一个。
例如我们可能得到这么一串数字（掷骰子10次）：1 6 3 5 2 7 3 5 2 4这串数字叫做可见状态链。
但是在隐马尔可夫模型中，我们不仅仅有这么一串可见状态链，还有一串隐含状态链。
在这个例子里，这串隐含状态链就是你用的骰子的序列。
比如，隐含状态链有可能是：D6 D8 D8 D6 D4 D8 D6 D6 D4 D8一般来说，HMM中说到的马尔可夫链其实是指隐含状态链，因为隐含状态（骰子）之间存在转换概率（transition probability）。
在我们这个例子里，D6的下一个状态是D4，D6，D8的概率都是1/3。
D4，D8的下一个状态是D4，D6，D8的转换概率也都一样是1/3。
这样设定是为了最开始容易说清楚，但是我们其实是可以随意设定转换概率的。
比如，我们可以这样定义，D6后面不能接D4，D6后面是D6的概率是0.9，是D8的概率是0.1。
这样就是一个新的HMM。
同样的，尽管可见状态之间没有转换概率，但是隐含状态和可见状态之间有一个概率叫做输出概率（emission probability）。
《信息隐藏技术》(任帅) 课件 第3、4章 基于三维模型的信息隐藏区域、基于数字图像与三维模型的信息
第三章 基于三维模型 的信息隐藏区域
1. 基于三维模型能量特性的信息隐藏区域 2. 基于三维模型结构特性的信息隐藏区域
第三章基于三维模型的信息隐藏区域 3.1 基于三维模型能量特性的信息隐藏区域
能量特性是三维模型信息隐藏区域选择所必须考虑的问 题,与算法的不可见性和鲁棒性有密切关系。现有关于能量 特 性区域的算法主要是基于载体小波域分解、网格频谱分析、 Laplace谱压缩等的能量特性来实现信息隐藏,比如经过DCT 变换后的载体包括直流分量和交流分量两部分。
第三章基于三维模型的信息隐藏区域
4.颜色场结构理论中的相关术语
1)颜色空间矩阵 载体图像进行颜色分离后,相同像素点分离出的多通道 颜色分量所组成的行矩阵称为颜色空间矩阵,记作Cij:
第三章基于三维模型的信息隐藏区域
2)整合模块 整合模块是一个矩阵集合,包含了与信息隐藏性能有关 的图像结构权重信息,用于生成整合矩阵。在基于数字图像 的信息隐藏系统中,整合模块要充分考虑信息隐藏系统的应 用要求。整合因素包括纹理信息和结构信息,整合规则如表 4-3所示。
第三章基于三维模型的信息隐藏区域 3.2 基于三维模型结构特性的信息隐藏区域
空间域算法通过改变三维模型几何属性、三维模型或面 片集的法向量及利用三维模型的属性信息和冗余性来隐藏信 息。可用于隐藏信息的几何属性包括顶点坐标、顶点到参考 点(线)的距离、顶点在其一环邻居中的位置、距离比或体积 比以及局部几何体素和全局几何体素。可见,空间域算法中 大多是利用载体的结构特性作为隐藏区域的。
第三章基于三维模型的信息隐藏区域
图3-1 曲率与视觉感的不一致性第三章基于三Fra bibliotek模型的信息隐藏区域
局部高度(LocalHeight,LH)是一种新的显著性度量方法, 用来测量某点的凸起程度,在特征点检测方面优于传统的曲 率概念。设定v 的R 邻居点集合为NR (v),缩写为NR ,则点v 的 局部高度由式(3-1)计算得出,其中C 为NR 中顶点所关联面片 的面积和。
改进的成对预测误差扩展可逆数据隐藏算法
Figure 2. On the method of traditional prediction error expansion 图 2. 关于传统预测误差扩展的方法
传统预测误差扩展的提取过程是嵌入秘密信息的逆过程，提取和图像恢复过程总结如下。首先，得
到嵌入秘密信息之后的预测误差，然后，原始图像的像素预测误差表示为
ei
=
eeii
, −
1,
ei +1,
if ei ∈{−1,0}
if ei > 0 if ei < 0
最后，图像像素恢复为 x=i xˆi + ei 。
2.2. 自适应嵌入和最佳预测误差选择相结合的预测误差扩展
针对传统预测误差进行改进，其中有自适应嵌入的预测误差扩展方法[17]和最佳预测误差选择的预测
误差扩展方法，将这两种方法结合起来就是这部分要进行描述得方法(AO-PEE)。
首先是自适应嵌入策略，为了更好地利用图像的冗余，提出了复杂度度量 ni，并且，只有当 ni < T 时嵌入的那一部分可以用来嵌入秘密信息，T 是预先选择的阈值且 T > 0。这相当于，对于每个像素(ni < T) 时，将根据传统预测误差扩展方法的嵌入程序进行处理。否则，也就是 ni ≥ T，像素会被忽略，值保持不 变。在这种方法中，是影响嵌入性能的重要因素。为了更好地利用平滑像素，T 被当作最小的正整数，
2) 提出了一种基于全封闭预测方法的计算像素局部区域纹理度高低的方法，3 × 3 的像素可以被分成 上下两部分，每部分分别计算相邻像素和被测像素差的绝对值之和，再将这两部分计算的值相加，在计
隐藏(隐写信息术)
16
为什么客观评价与主观评价不能很 好吻合？ 好吻合？
人眼对所看到物体的理解，不仅与生理因 人眼对所看到物体的理解， 素有关， 素有关，还在相当大的程度上取决于心理 因素 如“视而不见”，“听而不闻” 视而不见” 听而不闻” 对感兴趣的区域给予极大关注，对其它区 对感兴趣的区域给予极大关注， 域不在意 大脑对所接收的事务有一个过滤和取舍的 过程， 过程，目前计算机还无法很好地模拟此过 程
2010-8-19
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隐写工具
ftp://ftp.funet.fi/pub/crypt/steganograph y/ /stegano/index.htm 《互联网上常见的图像隐写软件》. 刘九芬, 陈嘉勇, 张卫明等，第二届中国可信计算与 信息安全学术会议，2006年.
2010-8-19
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1 信息隐藏概述 2 隐写术的基本原理 3 隐写术 4 隐写分析技术
信息隐藏实验五-stirmark与jsteg
Stirmark操作步骤
打开Media文件夹，有两个子文件夹Input和Output。 将待检测的图像放入\Media\Input\Images\Set1中。如图 双击\Bin\Benchmark中的StirMark Benchmark.exe（stirmark主 程序），程序自动运行，将待测图像的各种检测结果图像 放入\Media\Onput\Images\Set1中。
信息隐藏实验五(六)
2015年11月
实验内容(18:30-21:00)
一、Stirmark攻击工具介绍 二、Stirmark攻击实验 三、实现Jsteg的嵌入和提取算法（拓展）
一、Stirmark攻击工具介绍
Stirmark是一个检测水印算法鲁棒性的攻击工具。做法是： 给定嵌入水印的图像，Stirmark生成一定数量的修改图像， 这些被修改的图像被用来验证水印是否能被检测出。 攻击手段包括线性滤波、非线性滤波、剪切/拼接攻击、同 步性破坏攻击等。
LSB嵌入的改进
设定阈值 T ， T= 嵌入位置的八个邻居像素值之和 嵌入位置的像素值。若T>0，加1；若T<0，减1。
以0.5的概率加减1。
三、实现Jsteg的嵌入和提取算法
实验1.2：
实现Jsteg的嵌入和提取。
实验要求 ：
1. 2.
分析鲁棒性和隐蔽性，计算在不同JPEG压缩率下提取信息 的误码率。 计算在stirmark攻击下的误码率。
Stirmark操作步骤
Stirmark操作步骤
攻击结果如图：（命名：原始图_操作类型_参数，运行完后
Bin\Benchmark文件夹下生成的log日志文件记录了详细攻击策略信息）
二、Stirmark攻击实验 实验1.1：
研究和分析文本信息隐藏工具(IJCNIS-V5-N12-6)
I. J. Computer Network and Information Security, 2013, 12, 45-52Published Online October 2013 in MECS (/)DOI: 10.5815/ijcnis.2013.12.06Study and Analysis of Text Steganography ToolsIndradip BanerjeeDepartment of Computer Science & Engineering, National Institute of Technology,Durgapur, West Bengal, India.ibanerjee2001@Souvik BhattacharyyaDepartment Computer Science & Engineering,University Institute of Technology,Burdwan University, Burdwan, Indiasouvikbha@Gautam SanyalDepartment of Computer Science & Engineering, National Institute of Technology,Durgapur, West Bengal, India.nitgsanyal@Abstract — “Maintain the security of the secret information”, this words has been a great challenge inour day to day life. Sender can send messages regularly through a communication channel like Internet, drawsthe attention of third parties, hackers and crackers, perhaps causing attempts to break and expose the unusual messages. Steganography is a gifted region which is used for secured data transmission over any public media. Wide quantity of research work has been established by different researchers on steganography. Steganalysis is an art and science of detecting messages hidden using steganography. Some research work has also been remarked in the field of Steganalysis also. In this contribution, we have gone through steganalysis attack of some established text steganography tools.Index Term— Text Steganography, Text Steganalysis, Security, Cover Text, Stego TextI.I NTRODUC TIONInformation hiding is the ability to prevent or hidden certain aspects from being accessible to others excluding authentic user. It has many sub disciplines. One of the most important sub disciplines is steganography [1] which is derived from a work by Johannes Trithemus (1462-1516) entitled "Steganographia" and comes from the Greek language defined as "covered writing" [2]. It is an ancient art of hiding information in ways a message i s hidden in an innocent-looking cover media so that will not arouse an eavesdropper’s suspicion. Steganography diverges from cryptography in the sense that where cryptography focuses on keeping the contents of a message secret by encryption technique, steganography focuses on keeping the presence of a message secret [3], [4].Another form of information hiding is digital watermarking, which is the process that embeds data called a watermark, tag or label into a multimedia object such that watermark can be detected or extracted later to make an assertion about the object. The object may be an image, audio, video or text only [5], [6]. A hidden channel could be defined as a communications channel that transfers some kind of information using a method originally not intended to transfer this kind of information. Observers are unaware that a covert message is being communicated. Only the sender and recipient of the message notice it. Steganography works have been carried out on different media like images, video clips, text, music and sound [7], [4].In Image Steganography method the secret message is embedded into an image as noi se to it, which is nearly impossible to differentiate by human eyes [8], [9], [10]. In video steganography, same method may be used to embed a message [11], [12]. Audio steganography embeds the message into a cover audio file as noise at a frequency out of human hearing range [10]. One major category, perhaps the most difficult kind of steganography is text teganography or linguistic steganography because due to the lack of redundant information in a text compared to an image or audio. The text steganography is a method of using written natural language to conceal a secret message as defined by Chapman et al. [13].Figure 1: Types of Steganography46Study and Analysis of Text Steganography ToolsTEXT STEGANOGRAPHYThe affluence of electronic documented information available in the world as well as the exertion of serious linguistic analysis makes this an interesting medium for steganographic information hiding. Moreover the Text is one of the ancient media used in steganography. Letters, books and telegrams hide secret messages within their texts in earlier time i.e. before the electronic age comes. Text steganography refers to the hiding of information within text i.e. character-based messages. There are three basic categories of text steganography (Fig. 1) maintained here: format-based methods, random and statistical generation and linguistic methods.[14]i. Format-based methods [14]:This methods use the physical formatting of text as a space in which to hide information. Format-based methods usually modify existing text for hiding the steganographic text. Insertion of spaces or non-displayed characters, careful errors tinny throughout the text and resizing of fonts are some of the many format-based methods used in text steganography.ii. Random and statistical generation method [14]: This avoid comparison with a known plaintext, steganographers often resort to generating their own cover texts. Character sequences method hide the information within character sequences.iii. Linguistic methods [14]:The affluence of electronic documented information available in the world as well as the exertion of serious linguistic analysis makes this an interesting medium for steganographic information hiding.In case of text steganography, firstly, a secret message will be covered up in a cover-text by applying an embedding algorithm to produce a stego-text. The stego-text will then be transmitted by a communication channel to a receiver.TEXT STEGANALYS ISThe usage of text media, as a cover channel for secret communication, has drawn more attention [15]. This attention in turn creates increasing concerns on text steganalysis. At present, it is harder to find secret messages in texts compared with other types of multimedia files, such as image, video and audio [16-21]. In general, text steganalysis exploits the fact that embedding information usually changes some statistical properties of stego texts; therefore it is vital to perceive the modifications of stego texts. Previous work on text steganalysis could be roughly classified into three categories: format- based [22, 23], invisible character-based [24-26] and linguistics, respectively. Different from the former two categories, linguistic steganalysi s attempts to detect covert messages in natural language texts. In the case of linguistic steganography, lexical, syntactic, or semantic properties of texts are manipulated to conceal information while their meanings are preserved as much as possible[27].Due to the diversity of syntax and the polysemia of semantics in natural language, it is difficult to observe the alterations in stego texts. So far, many linguistic steganalysis methods have been proposed. In these methods, special features are designed to extend semantic or syntactical changes of stego texts. For example, Z.L. Chen [28] et al. designed the N-window mutual information matrix as the detection feature to detect semantic steganagraphy algorithms. Furthermore, they used the word entropy and the change of the word location as the semantic features [29, 30], which improved the detection rates of their methods. Similarly, C.M. Taskiran et al [31] used the probabilistic context-free grammar to design the special features in order to attack on syntax steganography algorithms. In the work mentioned above, designed features strongly affect the final performances and they can merely reveal local properties of texts. Consequently, when the size of a text is large enough, differences between Natural texts (NTs) and Stego texts (STs) are evident, thus the detection performances of the mentioned methods are acceptable. Whereas, when the sizes of texts become small, the detection rates decrease dramatically and cannot be satisfied for applications. In addition, some steganographic tool s have been improved in the aspectsof semantic and syntax for better camouflage [32]. Therefore, linguistic steganalysi s still needs further research to resolve these problems. Some more work on Text Steganalysi s has been discussed below.A. Linguistic Steganalysis Based on Meta Features and Immune Mechanism [33]: Linguistic steganalysi s depends on efficient detection features due to the diversity of syntax and the polysemia of semantics in natural language processing. This paper presents a novel linguistics steganalysis approach based on Meta features and immune clone mechanism. Firstly, Meta features are used to represent texts. Then immune clone mechanism is exploited to select appropriate features so as to constitute effective detectors. Our approach employed Meta features as detection features, which is an opposite view from the previous literatures. Moreover, the immune training process consi sts of two phases which can identify respectively two kinds of stego texts. The constituted detectors have the capableof blind steganalysis to a certain extent. Experiments show that the proposed approach gets better performance than typical existing methods, especially in detecting short texts. When sizes of texts are confined to 3kB, detection accuracies have exceeded 95.B. Research on Steganalysis for Text Steganography Based on Font Format [34]: In the research area of text steganography, algorithms based on font format have advantages of great capacity, good imperceptibility and wide application range. However, little work on steganalysis for such algorithms has been reported in the literature. Based on the fact that the statistic features of font format will be changed after using font-format-based steganographic algorithms, we present a novel Support Vector Machine-based steganalysis algorithm to detect whether hidden information exists or not. This algorithm can not only effectively detect the existence of hidden information,but also estimate the hidden information length according to variations of font attribute value. As shown by experimental results, the detection accuracy of our algorithm reaches as high as 99.3 percent when the hidden information length is at least 16 bits.The dimensionality of data from text file is normally huge; it is unrealistic to use the data directly for steganalysis. A feasible approach is to extract certain amount of data from the text and use them to represent the text itself for steganalysis. The features for steganalysis should reflect minor distortions associated with data hiding.Moments based FeatureTo construct the features of both cover and stego or suspicious text several moments of the series has been computed. In mathematics, a moment is, loosely speaking, a quantitative measure of the shape of a set of points. The "second moment", for example, is widely used and measures the "width" of a set of points in one dimension or in higher dimensions measures the shape of a cloud of points as it could be fit by an ellipsoid. Other moments describe other aspects of a distribution such as how the distribution is skewed from its mean, or peaked. There are two ways of viewing moments [35], one based on statistics and one based on arbitrary functions such as f (x ) or f (x , y ).Statistical view: Moments are the statistical expectation of certain power functions of a random variable. The most common moment is the mean which is just the expected value of a random variable as given in 1.∫∞∞−==dx x xf X E )(][µ(1)where f (x ) is the probability density function of continuous random variable X . More generally, moments of order p = 0, 1, 2, … can be calculated as m p = E[X p ].These are sometimes referred to as the raw moments. There are other kinds of moments that are often useful. One of these is the central moments])[(p p X E µµ−=. The best knowncentral moment is the second, which is known as the variance, given in 2.21222)()(µµσ−=−=∫m dx x f x(2)Two less common statistical measures, skewness and kurtosis, are based on the third and fourth central moments. Moments are easily extended to two or more dimensions as shown in 3.∫∫==dxdy y x f y x Y X E m qp q p pq ),(][ (3)Here f (x , y ) is the joint pdf.Estimation: However, moments are easy to estimate from a set of measurements, x i . The p -th moment is estimated as given in 4 and 5.∑−=Ni p ip xNm 11 (4)(Often 1/N is left out of the definition) and the p -th central moment is estimated as∑−=ip ip x xN)(1µ(5)x is the average of the measurements, which is theusual estimate of the mean. The second central moment gives the variance of a set of data 22µ=s . For multidimensional distributions, the first and second order moments give estimates of the mean vector and covariance matrix. The order of moments in two dimensions is given by p +q , so for moments above 0, there is more than one of a given order. For example, m 20, m 11, and m 02are the three moments of order 2.Non-statistical view: This view is not based on probability and expected values, but most of the same ideas still hold. For any arbitrary function f (x ), one may compute moments using the equation 6 or for a 2-D function using 7.∫∞∞−=dx x f xm pp )((6)∫∫=dxdy y x f y x m q p pq ),((7)Notice now that to find the mean value of f (x ), one must use m 1/m 0, since f (x ) is not normalized to area 1 like the pdf. Likewise, for higher order moments it is common to normalize these moments by dividing by m 0(or m 00). This allows one to compute moments which depend only on the shape and not the magnitude of f (x ). The result of normalizing moments gives measures which contain information about the shape or distribution (not probability dist.) of f (x ).Digital approximation: For digitized data, we must replace the integral with a summation over the domain covered by the data. The 2-D approximation is written in 8.∑∑∑∑−−−−==M i Nj q p M i N j q jp ij i pq j i j i f y x y x f m 1111),(),((8)If f (x , y ) is a binary matrix function of an object, the area is m 00, the x and y centroids are 9 and 10.0010/m m x =(9)0001/m m y = (10)To implement the attack of text Steganography we use some stego text from some available steganography tools which are discussed below.SNOW DOS 32 [37]: The encoding system used by snow depend on the fact that spaces and tabs (known as whitespace ), when appearing at the end of lines, are invisible when presented in pretty well all text viewing programs. This allows messages to be hidden in ASCII text without affecting the text's visual representation. And since trailing spaces and tabs occasionally occur naturally, their existence should not be sufficient to immediately alert an observer who stumbles across them.wbS tego4.3open [36]: This module of steganography has been published under the GNU General Public License (GPL). wbStego uses a custom mechanism for localization. All information is stored in the data file, which consists of a number of blocks, all introduced by a 3 byte header specifying the size of the block. The file is a terminated by 3 bytes set to 0, i.e. a block header without data block.In this paper, we have analyzed the text steganalysi s by the help of statistical moment’s technique using two of steganography tools. After wide research on steganography by author’s previous work [11], [12], [13], [14], [15], [16], [17], [18], [33], [34], author is going to start work on the analysis part of text steganography.This paper is organized into the following sections. Section II describes the proposed model. Analyses of the results are in section III. The last section descries the concluded part of the work.II. P ROPOSED M ODELText steganalysis, at all this paper exactly deals with, uses two steganography tools (SNOW DOS 32 and wbStego4.3open). We have selected a cover and then create stego text by inserting various length of secret message. After that find out the moments up to 10 orders and observed that wbStego4.3open is better than SNOW DOS 32 at the side of embedding capacity.Figure 4: 2nd Order Moment values of SNOW & wbStego4.3open.Here we have observed that the value of moment in various length has changes. Simultaniously it also occurs in between 2nd order moment to 10th order moment which is furnished in Figure 2 and Figure 3 of SNOW DOS 32 and wbStego4.3 open steganography softwares.A. Solution MethodologyThe proposed system involves two software windows i.e. SNOW DOS 32 and wbStego4.3open. The user will be someone who is aware with the process of information hiding and will have adequate knowledge of steganography systems. The user first selects the plain text message from a file or enters text in specified area of software, another text to be used as the carrier (cover text). Then every tool will hide the message in the selected cover text and will procedure the stego text. Then create stego in various length of message and find out the moments which are shown in Fig. 2 and 3. After the comparison in between these values it has been observed that the system wbStego4.3open has minimum changes found for each order of moment (Fig. 4, Fig. 5), whereas the SNOW DOS 32 graph is decreasing for increase of embedding capacity.III.E XPERIMENTAL R ESULTSThe steganalyzer has been designed based on a training set and using various text steganographic tools. The steganographic tools used here SNOW DOS 32 & wbStego4.3open. In the experiments one cover and 30 input message were used for training and 20 cover text for testing. These experiments are performed using a large data set of text document obtained from publicly available websites. The data set i s categorized with respect to different features of the text to determine their potential impact on steganalysis performance. Fig.6 and Fig.7 shows that the graphical representation of 2nd Order Moment of SNOW DOS 32 and wbStego4.3open. Here it has been observe that the graph SNOW is decreasing for high embedding where as the wbStego graph is increasing for high embedding. So it has proved that the performance and capasity of wbStego is better than SNOW.IV.C ONCLUSIONSIn this paper, text based steganalysi s techniques of some module is tested based on moments and other similarity measure feature to evaluate what is the best. The plane text has been selected as an estimate of the cover-object. Next step is to use stati stical, invariant and other similarity measure features to measure the distortion and to determine the presence of hidden information in a text. Results from moments with numerous text series showed that the proposed steganalysis algorithm provides significantly better analysi s rates than existing ones. The author’s future goal is to compare these tool s with author’s own generic module of steganography.Figure 5: 7th Order Moment values of SNOW &wbStego4.3open.Figure 6: 2nd Order Moment graph of wbStego4.3open.Figure 7: 2nd Order Moment graph of SNOW.R EFERENCES[1]Fabien A.P. Petitcolas, Ross J. Anderson, MarkusG. Kuhn: Information Hiding—A Survey,Proceedings of the IEEE, Vol. 87, No. 7, July1999, pp. 1062-1078, ISSN 0018-9219.[2]K. Bennett. Linguistic steganography: Survey,analysis, and robustness concerns for hiding information in text. Purdue University, CERIASTech. Report, 2004.[3]Ross J. Anderson. and Fabien A.P.Petitcolas. Onthe limits of steganography. IEEE Journal onSelected Areas in Communications (J-SA C),Special Issue on Copyright and Privacy Protection,16:474–481, 1998.[4]JHP Eloff T Mrkel and MS Olivier. An overviewof image steganography. In Proceedings of thefifth annual Information Security South AfricaConference, South Africa, 2005.[5]S.P.Mohanty. Digital watermarking: A tutorialreview. International Journal of Digital Evidence,Fall 2003, 2003.[6]N.F.Johnson. and S. Jajodia. Steganography:seeing the unseen. IEEE Computer, 16:26–34,1998.[7]Kran Bailey Kevin Curran. An evaluation of imagebased steganography methods. InternationalJournal of Digital Evidence,Fall 2003, 2003.[8] D. Kahn. The codebreakers - the comprehensivehistory of secret communication from ancient times to the internet. Scribner, 1996.[9]Z. Duric N. F. Johnson and S. Jajodia. Informationhiding: Steganography and digital watermarking -attacks and countermeasures. Kluwer Academic,2001.[10]S. Low N.F. Maxemchuk J.T. Brassil and L.O.Gorman. Electronic marking and identificationtechniques to discourage document copying. IEEEJournal on Selected Areas in Communications,13:1495–1504, 1995.[11]G. Doerr and J.L. Dugelay. Security pitfalls offramebyframe approaches to video watermarking.IEEE Transactions on Signal Processing, Supplement on Secure Media., 52:2955–2964,2004.[12]G. Doerr and J.L. Dugelay. A guide tour of videowatermarking. Signal Processing: ImageCommunication., 18:263–282, 2003.[13]Kran Bailey Kevin Curran. An evaluation of imagebased steganography methods. 1999.[14]Krista Bennett (2004). " Linguistic Steganography:Survey, Analysis, and Robustness Concerns forHiding Information in Text". CERIAS TR 2004-13.[15]F.A.P. Petitcolas, R.J. Anderson, M.G. Kuhn,“Information hiding - a survey”, Proceedings ofthe IEEE, Vol.87, No.7, pp.1062–1078, 1999. [16]C. Kraetzer, J. Dittmann, “Pros and cons ofmelcepstrum based audio steganalysis using SVMclassification”, The 9th International Workshop onInformation Hiding, Saint Malo, France, pp.359–377, 2007.[17]M.E. Choubassi, P. Moulin, “Noniterativealgorithms for sensitivity analysis attacks”, IEEETransactions on Information Forensics and Security, Vol.2, No.3, pp.113–126, 2007.[18]O.H. Kocal, E. Avcibas, “Chaotic-type features forspeech steganalysis”, IEEE Transactions on Information Forensics and Security, Vol.3, No.4,pp.651–661, 2008.[19]Z.J. Wu, Y. Hu, X.X. Niu, H.X. Duan, X. Li,“Information hiding technique based speech securecommunication over PSTN”, Chinese Journal ofElectronics, Vol.15, No.1, pp.108–112, 2009. [20]H. Shan, K. Darko, “An estimation attack oncontent-based video fingerprinting”, Transactionson Data Hiding and Multimedia Security II,Vol.4499, No.2007, pp.35–47, 2007.[21]R. Bohme, “Weighted stego-image steganalysi sfor JPEG covers”, The 10th International Workshop on Information Hiding, Santa Barbara,California, USA, pp.178–194, 2008.[22]L.J. Li, L.S. Huang, X.X. Zhao, W. Yang, Z.L.Chen, “A statistical attack on a kind of word-shifttext-steganography”, The 4th International Conference on Intelligent Information Hiding andMultimedia Signal Processing, Harbin, China,pp.1503–1507, 2008.[23]L.Y. Xiang, X.M. Sun, G. Luo, C. Gan, “Researchon steganalysis for text steganography based onfont format”, The 3rd International Symposium onInformation Assurance and Security, Manchester,United Kingdom, pp.490–495, 2007.[24]J.W. Huang, X.M. Sun, H. Huang, G. Luo,“Detection of hidden information in webpagesbased on randomness”, The 3rd InternationalSymposium on Information Assurance andSecurity, Manchester, United kingdom, pp.447–452, 2007.[25]H.J. Huang, X.M. Sun, Z.H. Li, G. Sun,“Detection of steganographic information in tagsof webpage”, The 2nd International Conference onScalable Information Systems, Brussels, Belgium,pp.325–328, 2007.[26]H.J. Huang, S.H. Zhong, X.M. Sun, “Steganalysisof information hidden in webpage based onhigher-order statistics”, Proceedings of theInternational Symposium on Electronic Commerceand Security, ISECS 2008, Guangzhou, China,pp.957–960, 2008.[27]M. Chapman, G.I. Davida, M. Rennhard, “Apractical and effective approach to large scaleautomated linguistic steganography”, The 4thInternational Conference on Information andCommunications Security, Venice, Italy, pp.156–165, 2007.[28]Z.L. Chen, L.S. Huang, Z.Z. Yu, W. Yang, L.J. Li,X.L. Zheng, X.X. Zhao, “Linguistic steganographydetection using statistical characteristics ofcorrelations between words”, The 11thInternational Workshop on Information Hiding,Darmstadt, Germany, pp.224–235, 2008.[29]Z.L. Chen, L.S. Huang, Z.S. Yu, X.X. Zhao, X.L.Zheng, “Effective linguistic steganographydetection”, The 8th IEEE International Conferenceon Computer and Information TechnologyWorkshops, Sydney, Australia, pp.224–229, 2008. [30]Z.L. Chen, L.S. Huang, Z.S. Yu, L.J. Li, W. Yang,“A statistical algorithm for linguisticsteganography detection based on distribution ofwords”, The 3rd International Conference onAvailability, Security, and Reliability, Barcelona,Spain, pp.558–563, 2008.[31]C.M. Taskiran, U. Topkara, M. Topkara, E.J. Delp,“Attacks on lexical natural languagesteganography systems”, Proceedings of SPIEInternational Society for Optical Engineering,Society of Photo-Optical InstrumentationEngineers, San Jose, USA, pp.97–105, 2006.[32]K. Bennett, “Linguistic steganography: Survey,analysis, and robustness concerns for hidinginformation in text”, Purdue University, Indiana,USA, 2004.[33]YA NG Hao and CAO Xianbin “ LinguisticSteganalysis Based on Meta Features and ImmuneMechanism “Chinese Journal of Electronics,Vol.19, No.4, Oct. 2010[34]Lingyun Xiang, Xingming Sun, Gang Luo, CanGan. “Research on Steganalysis for TextSteganography Based on Font Format”, The ThirdInternational Symposium on InformationAssurance and Security (IAS 2007), Manchester, United Kingdom , August 2007. [35] MOMENTS IN IMA GE PROCESSING Bob Bailey Nov. 2002 [36] Available online: http://home.tele2.at/wbailer/wbstego/wbs4devdoc .html [37] Available online: .au/snow/ [38] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. “Novel text steganography through special code generation.” In Proceedings of International Conference on Systemics,Cybernetics and Informatics (ICSCI-2011), Hyderabad,India., Jan 5-8, 2011. [39] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. “The text steganography using article mapping technique(AMT) and SSCE”. Journal of Global Research in Computer Science, 2, April 2011. [40] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. Design and implementation of a secure text based steganography model. In 9th annual Conference on Security and Management (SAM) under The 2010 World Congress in Computer Science,Computer Engineering and Applied Computing(WorldComp 2010), Las Vegas,USA, July 12-15,2010. [41] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. Implementation of a novel text based steganography model. In National Conference on Computing and Systems (NACCS), Dept. of Computer Science, The University of Burdwan, Burdwan,India., Jan 29, 2010. [42] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. A novel approach of secure text based steganography model using word mapping method(WMM). International Journal of Computer and Information Engineering 4:2 2010 - World Academy of Science, Engineering and Technology (WASET), 4:96103, Spring 2010. [43] Souvik Bhattacharyya, Indradip Banerjee, Arka Prokash Mazumdar and Gautam Sanyal. Text steganography using formatting character spacing. IJICS, 13, Decembar, 2010. [44] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. A survey of steganography and steganalysi s technique in image, text, audio and video as cover carrier. Journal of Global Research in Computer Science, 2, April 2011. [45] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. An Approach of Quantum Steganography through Special SSCE Code. International Journal of Computer and Information Engineering - World Academy of Science, Engineering and Technology (WASET), Issue 0080:2011, Article 175, Page: 939-946. [46] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. Text Steganography through Quantum Approach. In Journal on Wireless Networks And Computational Intelligence, Communications in Computer and Information Science, 2012, Volume 292, Part 7, 632-643, DOI:10.1007/978-3-642-31686-9_74 Springer-Verlag Berlin Heidelberg 2012.[47] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. "A Procedure of Text Steganography Using Indian Regional Language"Journal on "I. J. Computer Network and Information Security, 2012, v. 8, p. 65-73"Published Online August 2012 in MECS.Indradip Banerjee is a Research Scholar at National Institute ofTechnology, Durgapur, West Bengal, India. He received his MCA degree from IGNOU in 2009, PGDCA from IGNOU in 2008, MMM from Annamalai University in 2005 andBCA (Hons.) from The University of Burdwan in 2003. He is pursuing his PhD. in Engineering at Computer Science and Engineering Department, National Institute of Technology, Durgapur, West Bengal, India. His areas of interest are Steganography, Cryptography, Text Steganography, Image Steganography, Quantum Steganography and Steganalysis. He has published 16 research papers inInternational and National Journal s / Conferences. Souvik Bhattacharyya received his B.E. degree in Computer Science and Technology from B.E. College, Shibpur, India and M.Techdegree in Computer Science and Engineering from National Institute of Technology, Durgapur, India. Currently he is working as an Assistant Professor and In-Charge in Computer Science and Engineering Department at University Institute of Technology, The University of Burdwan. His areas ofinterest are Natural Language Processing, Network Security and Image Processing. He has published nearly 65 papers in International and National Journals / Conferences.Gautam Sanyal has received his B.E and M.Tech degree National Institute of Technology (NIT), Durgapur, India. He has receivedPh.D (Engg.) from Jadavpur University, Kolkata, India, in the area of Robot Vision. He possesses an experience of more than 25 years in the field of teaching and research. He has published nearly 150 papers in International and National Journals / Conferences. Two Ph.Ds (Engg) have already beenawarded under his guidance. At present he is guidingsix Ph.Ds scholars in the field of Steganography,。
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Stirmark操作步骤
打开Media文件夹，有两个子文件夹Input和Output。 将待检测的图像放入\Media\Input\Images\Set1中。如图 双击\Bin\Benchmark中的StirMark Benchmark.exe（stirmark主 程序），程序自动运行，将待测图像的各种检测结果图像 放入\Media\Onput\Images\Set1中。
LSB嵌入的改进
设定阈值 T ， T= 嵌入位置的八个邻居像素值之和 嵌入位置的像素值。若T>0，加1；若T<0，减1。
以0.5的概率加减1。
三、实现Jsteg的嵌入和提取算法
实验1.2：
实现Jsteg的嵌入和提取。
实验要求 ：
1. 2.
分析鲁棒性和隐蔽性，计算在不同JPEG压缩率下提取信息 的误码率。 计算在stirmark攻击下的误码率。
量出现连零实现的，如果改变DCT系数中”0”的话
，就不能很好的实现压缩. DCT系数中的”1”若变为”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 无法实现秘密消息的提取。
JSteg隐写对直方图的影响
嵌入前
嵌入后
特点：Jsteg隐写使得DCT系数中(2i,2i+1)的频率趋向一致。 由于这种统计直方图的异常，很容易被卡方攻击检测出来。
三、实现Jsteg的嵌入和提取算法（拓展）
JSteg隐写
基本思想：用秘密信息比特直接替换JPEG图像中量 化后DCT系数的最低比特位，但若量化后DCT系数为 0或者1，则不进行处理。（DCT系数的LSB嵌入）
JSteg隐写
(11)
(10)
(11)
嵌入方式示意图
JSteg隐写
DCT系数中0和1不处理:(原因) 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
实验报告内容
实验要求： 对DCT系数嵌入的两点和三点法，选择2种攻 击策略，采用Stirmark攻击，计算攻击后的误 码率。 实现Jsteg的嵌入和提取算法。
1. 2.
分析鲁棒性和隐蔽性，计算在不同JPEG压缩率下提取信 息的误码率。 计算在stirmark攻击下的误码率。
Stirmark操作步骤
Stirmark操作步骤
攻击结果如图：（命名：原始图_操作类型_参数，运行完后
Bin\Benchmark文件夹下生成的log日志文件记录了详细攻击策略信息）
二、Stirmark攻击实验 实验1.1：
对第4次实验（DCT系数隐写）的结果采用Stirmark 攻击（自己选择2种攻击策略），对攻击后的图片进行 信息的提取，计算攻击后的误码率。
信息隐藏实验五(六)
2015年11月
实验内容(18:30-21:00)
一、Stirmark攻击工具介绍 二、Stirmark攻击实验 三、实现Jsteg的嵌入和提取算法（拓展）
一、Stirmark攻击工具介绍
Stirmark是一个检测水印算法鲁棒性的攻击工具。做法是： 给定嵌入水印的图像，Stirmark生成一定数量的修改图像， 这些被修改的图像被用来验证水印是否能被检测出。 攻击手段包括线性滤波、非线性滤波、剪切/拼接攻击、同 步性破坏攻击等。