基于校准DCT特征的JPEG图像隐写分析

提出一种基于校准DCT特征和支持向量机的JPEG图像隐写分析方法。在JPEG图像的DCT域,采用联合概率密度计算DCT系数块内和块间相关性,联合低频AC系数统计特征,利用校准技术提取243维特征,然后使用支持向量机进行训练和分类。对三种常见的JPEG图像隐写算法(F5、Outguess、MB1)进行了二分类实验。实验结果表明:在嵌入率较低的情况,该算法的检测率优于其他算法。     

采用SVR模型进行嵌入率估计的隐写分析方法

为解决大多数通用隐写分析算法不能检测秘密信息长度的问题,提出了一种改进的能估计秘密信息长度的通用隐写分析方法。从隐写图中提取描述DCT域系数相关性的132维特征,用支持向量回归机学习图像特征和相应嵌入改变率之间的映射关系并建立模型,根据映射模型估计测试隐写图的嵌入改变率。使用典型的嵌入算法:F5、outguess与MB进行测验,仿真结果显示提出的秘密信息长度估计算法是切实可行的。     

基于多分类支持向量机的隐写域盲检测

提出一种区分隐写域(包括像素域、DCT域、DWT域)的盲检测方法,构造图像特征向量,建立一个多分类的支持向量机,根据特征向量对图像进行训练。该方法能够识别隐藏信息和其隐写域。实验结果表明,当嵌入容量达到20%时,识别率提高到85%以上。     

基于马尔可夫模型和支持向量机的JPEG图像隐写分析

提出了一种新的针对JPEG图像的通用隐写分析方法，利用马尔可夫模型，挖掘量化后的分块DCT系数中低频系数的相关性，提取出360维特征，然后采用支持向量机（SVM）分类方法进行识别。对四种公认的安全性较高的JPEG嵌入方法F5、Outguess、MB1和MB2进行隐写分析。在CorelDraw图像库的实验结果显示：该方法的检出率高、稳定性好且运算速度快。     

基于马尔可夫模型和特征融合的图像隐写分析

提出一种针对JPEG图像隐写的通用隐写分析方法.根据量化后分块DCT系数绝对值构造水平、垂直和zigzag方向的差分数组,利用三向差分数组马尔可夫模型挖掘量化后分块DCT块内邻近系数相关性,提取转移概率矩阵的特征.对三向特征加权融合后进行隐写分析,以提高分类性能.对安全性较高的JPEG隐写OutGuess和F5,在不同嵌入率下进行隐写分析.实验结果显示,引入特征融合后隐写分析的检出率明显提高.     

基于统计模型及SVM的低速率语音编码QIM隐写检测

QIM(Quantization Index Modulation,量化索引调制)隐写在标量或矢量量化时嵌入机密信息,可在语音压缩编码过程中进行高隐蔽性的信息隐藏,文中试图对该种隐写进行检测.文中发现该种隐写将导致压缩语音流中的音素分布特性发生改变,提出了音素向量空间模型和音素状态转移模型对音素分布特性进行了量化表示.基于所得量化特征并结合SVM(Support Vector Machine,支持向量机)构建了隐写检测器.针对典型的低速率语音编码标准G.729以及G.723.1的实验表明,文中方法性能远优于现有检测方法,实现了对QIM隐写的快速准确检测.     

基于时空相关性的视频隐写分析

视频中的扩频嵌入是一种典型的隐写方法,能够抵抗视频压缩、噪声添加等多种攻击。为了有效检测利用该方法嵌入的隐藏信息,将隐写过程建模为加性高斯噪声的叠加过程,分析了噪声叠加对视频序列的时间相关性和空间相关性的影响,提出了基于时空相关性的隐写分析算法。该算法利用视频序列中各帧的四方向差分直方图度量隐写对空间相关性的影响,同时利用相邻帧帧间差分的直方图度量隐写对时间相关性的影响。与现有算法相比,本文算法实现简单,实验结果表明本文算法具有更好的检测性能。     

彩色图像通用隐写分析的多类统计特征

提出了用于彩色图像通用隐写分析的3类统计特征。针对隐写所导致的图像DCT系数分布、空域像素值平坦性以及颜色空间一致性的改变,从系数分布模型背离程度、空域相关性和不同颜色分量间的相关性3个方面设计能够体现原始载体和含密图像差异的特征,提取了10维特征向量。这些特征对隐写行为较为敏感、涵盖面广,且维数低、计算方便。在这些特征的基础上,可利用支持向量机分析彩色JPEG图像是否含有秘密信息。实验结果表明,基于这些特征的通用隐写分析方法可以准确、高效地检测以Jstge、F5或MB隐写方法嵌入的秘密信息,辨别隐写方法种类,具有很好的通用性。     

以直方图差值序列为特征的JPEG MB2隐写分析

基于JPEG图像的离散余弦变换(DCT)系数直方图差值序列,提出一种新的检测JPEG MB2隐写术的方法。该算法定义JPEG图像的DCT系数一阶直方图与其Cauchy拟合分布模型的系数直方图之间的差值序列作为相关性度量,在此基础上构造区分隐写图像和载体图像的分类特征序列。在特征分析中,通过Hilbert-Huang变换对特征序列进行经验模式分解,构建了基于Hilbert谱的特征向量。实验结果表明,基于该特征向量的支持向量机(SVM)对MB2隐写算法检测平均准确率达到84.48%,该方法也适用于JPEG MB1的检测。     

基于运动估计的视频隐写检测算法

提出一种基于运动估计的的视频隐写检测算法。通过块均方误差的变化研究信息嵌入对运动估计的影响,发现运动矢量对隐写具有敏感性,且分块越小,对隐写越敏感。反映视频时变特性的运动矢量场被作为隐写检测中视频数据的表征,特征提取过程中,先取定分块大小,使用最小均方误差块匹配运动估计算法,得到运动矢量场。再提取运动矢量场3个方向相邻元素的共生矩阵,使用共生矩阵的主对角线及其相邻元素作为特征。使用支持向量机分类器实施检测,实验结果表明文中算法与Budhia的算法相比,具有更好的检测性能。     

An improved approach to steganalysis of JPEG images

Steganography secretly embeds additional information in digital products, the potential for covert dissemination of malicious software, mobile code, or information is great. To combat the threat posed by steganography, steganalysis aims at the exposure of the stealthy communication. In this paper, a new scheme is proposed for steganalysis of JPEG images, which, being the most common image format, is believed to be widely used for steganography purposes as there are many free or commercial tools for producing steganography using JPEG covers.First, a recently proposed Markov approach [27] is expanded to the inter-block of the discrete cosine transform (DCT) and to the discrete wavelet transform (DWT). The features on the joint distributions of the transform coefficients and the features on the polynomial fitting errors of the histogram of the DCT coefficients are also extracted. All features are called original ExPanded Features (EPF). Next, the EPF features are extracted from the calibrated version; these are called reference EPF features. The difference between the original and the reference EPF features is calculated, and then the original EPF features and the difference are merged to form the feature vector for classification.To handle the large number of developed features, the feature selection method of support vector machine recursive feature elimination (SVM-RFE) and a method of multi-class support vector machine recursive feature elimination (MSVM-RFE) are used to select features for binary classification and multi-class classification, respectively. Finally, support vector machines are applied to the selected features for detecting stego-images.Experimental results show that, in comparison to the Markov approach [27], this new scheme remarkably improves the detection performance on several JPEG-based steganographic systems, including JPHS, CryptoBola, F5, Steghide, and Model based steganography.     

Digital video steganalysis by subtractive prediction error adjacency matrix

Video has become an important cover for steganography for its large volume. There are two main categories among existing methods for detecting steganography which embeds in the spatial domain of videos. One category focuses on the spatial redundancy and the other one mainly focuses on the temporal redundancy. This paper presents a novel method which considers both the spatial and the temporal redundancy for video steganalysis. Firstly, model of spread spectrum steganography is provided. PEF (Prediction Error Frame) is then chosen to suppress the temporal redundancy of the video content. Differential filtering between adjacent samples in PEFs is employed to further suppress the spatial redundancy. Finally, Dependencies between adjacent samples in a PEF are modeled by a first-order Markov chain, and subsets of the empirical matrices are then employed as features for a steganalyzer with classifier of SVM (Support Vector Machine). Experimental results demonstrate that for uncompressed videos, the novel features perform better than previous video steganalytic works, and similar to the well-known SPAM (Subtractive Pixel Adjacency Model) features which are originally designed for image steganalysis. For videos compressed with distortion, the novel features perform better than other features tested.     

基于多样性特征的JPEG图像隐写分析

随着隐写分析技术的发展,新的特征提取算法不断出现,但目前还没有一种较好的通用特征能对JPEG图像进行有效的隐写分析。针对上述问题,提出一种从多域空间提取特征的通用隐写分析算法。采用残差共生矩阵与直方图统计函数计算DCT域、空域、小波域各域系数(像素)之间的依赖性关系,并结合校准方式从中提取特征。对多样性特征维数高的问题,采用前向选择与穷举结合的方法对其降维,以提高分类精度与节约分类时间。对4种典型的JPEG隐写算法在小嵌入率下进行实验,结果表明,与已有的检测方法相比,多域空间提取的多样性特征检测准确率能提高2%以上,适应性更广。     

基于Gabor滤波特征和支持向量机的人脸检测

人脸检测是人脸识别与图像及视频检索的一项重要任务。论文提出了一种基于Gabor滤波特征和支持向量机的正面人脸检测方法。算法首先利用了Gabor滤波器的良好的空间位置与方向的选择特性,采用了四种方向的Gabor滤波器提取人脸样本图像特征并用PCA方法对特征降维,然后用已降维的特征训练支持向量机分类器。最后应用SVM分类检测人脸。实验结果证明该方法行是十分有效的。     

基于MCM和HVS的彩色图像盲隐写分析算法

针对典型算法在描述DCT域相关性方面存在的不足,所引起的隐写检测综合性能不理想的问题,将MCM与HVS进行有机结合,提出一种JPEG彩色图像通用隐写分析算法。设计8-邻域MCM型,全面描述DCT系数之间的相关性,基于HVS分别在YCbCr模型空间Y分量抽取Markov状态转移矩、在其相应RGB三通道抽取Markov状态转移矩的主对角线邻域相似熵作为特征统计量,并进行合理“绑定”,采用PCA技术对其进行优化选择,构建高效分类特征向量。实验结果表明该算法对于Jsteg、F5、Outguess、MB1、MB2攻击具有较高的可靠性和检测正确率。     

特征选择在隐秘图像检测中的应用

提出一种基于遗传算法和多超球面一类支持向量机的隐秘图像检测方案。为了得到最能反映分类本质的特征从而有效实现分类识别,采用遗传算法进行图像特征选择,将支持向量机的分类效果作为适应度函数值返回,指导遗传算法搜索最优的特征选择方案。实验结果表明,与仅采用支持向量机分类而未进行特征选择的隐秘检测方案相比,该方案提高了隐秘图像检测的识别率。     

A Universal Image Steganalysis System Based On Double Sparse Representation Classification (DSRC)

Achieving high rates of detection in low rates of embedding is still a challenging problem in many steganalysis systems. The newly proposed steganalysis system based on sparse representation classifier has shown remarkable detection rates in low embedding rate. In this paper, we propose a new steganalysis system based on double sparse representation classifier. We compare our proposed method with other steganalysis systems which use different classifier (including nearest neighbor, support vector machine, ensemble support vector machine and sparse representation). In all of our experiments, input features to the classifier are fixed and the ability of classifier is examined. Also we provide a complexity analysis in terms of execution time for different classifier. In most of experiments, our proposed method shows superior performance in terms of detection rate and complexity for low embedding rates.     

视频文字大小自适应提取算法*

视频文字大小自适应提取算法基于离散傅里叶变换(discrete Fourier transform, DFT)特征、多分辨率处理及支持向量机分类技术。算法在不同分辨率下结合梯度信息、文字边界定位技术提取出文字候选区域,然后用支持向量机对于候选图像块DFT特征作进一步分类。结果表明,该算法能提取出视频图像中不同大小的文字,识别率优于以小波、灰度、离散余弦变换系数(discrete cosine transform, DCT)等为纹理特征的算法。     

基于双向马尔可夫模型的JPEG图象隐写分析

提出了一种基于双向马尔可夫模型的JPEG图象的通用隐写分析方法,利用量化后分块DCT系数的中低频系数间的相关性,提取DCT块内和块间的特征,采用阈值贝叶斯分类方法进行识别,并且与SVM分类器的识别效果进行了比较。针对4种公认的JPEG嵌入方法——F5、Outguess、MB1和MB2进行隐写分析,在CorelDraw图象库上做实验,取得了很好的性能。     

Digital image splicing detection based on Markov features in DCT and DWT domain

Image splicing is very common and fundamental in image tampering. To recover people's trust in digital images, the detection of image splicing is in great need. In this paper, a Markov based approach is proposed to detect this specific artifact. Firstly, the original Markov features generated from the transition probability matrices in DCT domain by Shi et al. is expanded to capture not only the intra-block but also the inter-block correlation between block DCT coefficients. Then, more features are constructed in DWT domain to characterize the three kinds of dependency among wavelet coefficients across positions, scales and orientations. After that, feature selection method SVM-RFE is used to fulfill the task of feature reduction, making the computational cost more manageable. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the final dimensionality-reduced feature vector. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.