Pros and Cons of Mel-cepstrum Based Audio Steganalysis Using SVM Classification
While image steganalysis has become a well researched domain in the last years, audio steganalysis still lacks a large scale attentiveness. This is astonishing since digital audio signals are, due to their stream-like composition and the high data rate, a
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Abstract. While image steganalysis has become a well researched domain in the last years, audio steganalysis still lacks a large scale attentiveness. This is astonishing since digital audio signals are, due to their stream-like composition and the high data rate, appropriate covers for steganographic methods. In this work one of the first case studies in audio steganalysis with a large number of information hiding algorithms is conducted. The applied trained detector approach, using a SVM (support vector machine) based classification on feature sets generated by fusion of time domain and Mel-cepstral domain features, is evaluated for its quality as a universal steganalysis tool as well as a application specific steganalysis tool for VoIP steganography (considering selected signal modifications with and without steganographic processing of audio data). The results from these evaluations are used to derive important directions for further research for universal and application specific audio steganalysis.
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Introduction and State of the Art
When comparing steganalytical techniques a well used classification is to group them into specific and universal steganalysis techniques [1]. In the image domain a large number of examples for both classes can be found as well as research building “composite” steganalysis techniques by fusing existing techniques as described by Kharrazi et. al in [1]. In the research presented in [1] a fusion of steganalytical approaches on different levels (pre-classification and post-classification (in measurement or abstract level)) in image steganalysis is considered. This has been done by addressing the question “How to combine different (special and universal) steganalysers to gain an improved classification reliability?”. While such mature research exists in the domain of image steganalysis, the domain of audio steganalysis is much less considered in literature. This fact is quite remarkable for two reasons. The first one is the existence of advanced audio steganography schemes. The second one is the very nature of audio material as a high capacity data stream which allows for scientifically challenging statistical analysis. Especially inter-window analysis (considering the evolvement of the signal over time), which is only possible on this continuous media, distinguish audio signals from the image domain. T. Furon et al. (Eds.): IH 2007, LNCS 4567, pp. 359–377, 2007. c Springer-Verlag Berlin Heidelberg 2007
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C. Kraetzer and J. Dittmann
The research presented in this work is based on the audio steganography approach introduced in [2]. The audio steganalysis tool (AAST; AMSL Audio Steganalysis Toolset) introduced there in the context of VoIP steganography and steganalysis is enhanced and used here to perform a set of tests to further evaluate its performance in intra-window based universal audio steganalysis. The current version of the AAST uses a SVM classification on pre-trained models for classifying audio signals into un-marked signals and signals marked by known information hiding algorithms
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