摘要
提出了一种基于DCT域统计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,以马尔可夫模型描述DCT系数块内相关性,以联合概率密度描述DCT系块间相关性,利用校准技术提取89维特征,然后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。
A steganalysis algorithm based on DCT feature and support vector machine for JPEG image was proposed.In DCT domain for JPEG images,the Markov model was used to calculate intrablock correlations,and the joint probability density matrix was used to calculate interblock correlations,the calibration techniques was applied to extract 89-dimensional features,and then the support vector machine was utilized to train and classify.The experimental results for three kinds of popular JPEG steganographic schemes（F5,Outguess,MB1）had demonstrated that the detection rate of the proposed algorithm is better than the other algorithms in the case of low embedding rate.
出处
《计算机与数字工程》
2015年第12期2266-2270,共5页
Computer & Digital Engineering
