While image steganalysis has become a well researched domain in the last years, audio steganalysis still lacks a large scale atten-tiveness. 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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