文章基本信息

标题：Intra Block and Inter Block Neighboring Joint Density Based Approach for Jpeg Steganalysis
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作者：Arun R ; Nithin Ravi S ; Thiruppathi K 等
期刊名称：International Journal on Soft Computing
电子版ISSN：2229-7103
出版年度：2012
卷号：3
期号：2
DOI：10.5121/ijsc.2012.3206
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Steganalysis is the method used to detect the presence of any hidden message in a cover medium. A novel approach based on feature mining on the discrete cosine transform (DCT) domain based approach, machine learning for steganalysis of JPEG images is proposed. The neighboring joint density on both intra-block and inter-block are extracted from the DCT coefficient array. After the feature space has been constructed, it uses SVM like binary classifier for training and classification. The performance of the proposed method on different Steganographic systems named F5, Pixel Value Differencing, Model Based Steganography with and without deblocking, JPHS, Steghide etc are analyzed. Individually each feature and combined features classification accuracy is checked and concludes which provides better classification.
关键词：Steganography; Steganalysis; DCT; PVD; MB1; MB2;F5; JPHS; Steghide.