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Abstract: |
Along with the popular usage of JPEG images, steganography algorithms for JPEG images emerge increasingly nowadays, such as F5, MB1, Outguess. Leveraging on previous work, in this paper we present a new universal steganalysis method based on multi-view features discovery and Support Vector Machine（SVM） learning. Features from spatial domain, DFT domain and DCT domain are exploited respectively. Specifically, statistical moments of the grey level co-occurrence matrix, slope of the power spectrum curve in an image’s DFT domain and model parameters of DCT AC coefficients are regarded as the multi-view features. Such features which are extracted from an image and its corresponding predicted image are fused to form a 12-dimensional vector. The feature vector is then fed to a SVM classifier. Extensive experiments, including analysis on three popular steganography algorithms—F5, Outguess and MB1, are conducted. Experimental results show that the proposed approach outperforms the other three existing schemes.
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