MP3 audio steganalysis

Abstract

MP3, one of the most widely used digital audio formats, provides a high compression ratio with faithful quality. The widespread use enables MP3 audio files to become excellent covers to carry hidden information in audio steganography on the Internet. Our research, however, indicates that there are few steganalysis methods proposed to detect audio steganograms and that steganalysis methods for the information-hiding behavior in MP3 audio are particularly scarce. In this paper we propose a comprehensive approach to steganalysis of MP3 audio files by deriving a combination of features from quantized MDCT coefficients. We design frequency-based subband moment statistical features, accumulative Markov transition features, and accumulative neighboring joint density features on second-order derivatives. We also model the distortion by extracting the shape parameters of generalized Gaussian density from individual frames. Different feature selection algorithms are applied to improve detection accuracy. Signal complexity and modification density are introduced to provide a comprehensive evaluation. Experimental results show that our approach is successful in discriminating MP3 covers and the steganograms generated by the steganographic tool, MP3Stego, in each category of signal complexity, especially for the audio streams with high signal complexities that are generally more difficult to steganalyze.