Abstract
Steganography, the means for covert communication, creates a potential problem when it is misused for planning criminal activities. Differentiating anomalous audio document from pure audio document is difficult. This paper presents a Genetic Algorithm based approach to audio steganalysis. The basic idea is that the various audio quality metrics calculated on cover audio signals and on stego audio signals vis-a-vis their denoised versions are statistically different. GA is employed to derive a set of classification rules from audio data using these audio quality metrics, and the support-confidence framework is utilised as a fitness function to judge the quality of each rule. The generated rules are then used to detect or classify the audio documents in a real-time environment Experimental results show that the proposed technique provides promising detection rates.
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