Feature Mining and Intelligent Computing for MP3 Steganalysis

MP3 allows a high compression ratio while providing high fidelity. As it has become one of the most popular digital audio formats, MP3 is also conceivably a most utilized carrier for audio steganography, therefore, MP3 steganalysis is a topic deserving attention. In this paper, we propose a scheme for steganalysis of MP3Stego based on feature mining and pattern recognition techniques. We first extract the moment statistical features of GGD shape parameters of the MDCT sub-band coefficients, as well as the moment statistical features, neighboring joint densities, and Markov transition features of the second order derivatives of the MDCT coefficients on MPEG-1 Audio Layer 3. Support vector machines (SVM) are applied to these features for detection. Experimental results show that our method can successfully discriminate the steganograms created by using MP3stego from their MP3 covers, even with fairly low embedding ratio.