15 November 2007 Steganalysis method based on SVM and statistic model in contourlet domain
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Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67864C (2007) https://doi.org/10.1117/12.751199
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
There is an urgent demand on steganalysis, which analyzes if an image includes hidden information, or further decodes the hidden information. This paper proposes a steganalysis method based on statistics model in contourlet transform domain. The proposed method is a blind universal steganalysis method, which does not aim at specified steganography method. The contourlet coefficients of natural image shows obvious regularity which includes sparsity and clustering in subband and similarity across scales. The popular statistical model in wavelet subband is the generalized Gaussian distribution (GGD) model, which can capture the first-order statistical features in subband. While the GGD model can not characterize the dependency between coefficients. The proposed steganalysis method takes contourlet statistics in subband and dependency between contourlet coefficients into account. The dependencies are measured using mutual information. The selected features include parameters of GGD model in subband, the mutual information between coefficients. The classificator chose is Support Vector Machine (SVM). The experimental results show that the features used in the proposed method are valid, when the dependencies between contourlet coefficients are taken into account, the false positive rate is greatly lower than the case in which the dependencies are not considered.
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Hongping Xu, Hongping Xu, Jianhui Xuan, Jianhui Xuan, "Steganalysis method based on SVM and statistic model in contourlet domain", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67864C (15 November 2007); doi: 10.1117/12.751199; https://doi.org/10.1117/12.751199
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