StegoBreaker: Audio Steganalysis using Ensemble Autonomous Multi-Agent and Genetic Algorithm

Abstract

The goal of steganography is to avoid drawing suspicion to the transmission of a hidden message in multi-medium. This creates a potential problem when this technology is misused for planning criminal activities. Differentiating anomalous audio document (stego audio) from pure audio document (cover audio) is difficult and tedious. Steganalytic techniques strive to detect whether an audio contains a hidden message or not. This paper investigates the use of Genetic Algorithm (GA) to aid autonomous intelligent software agents capable of detecting any hidden information in audio files, automatically. This agent would make up the Detection Agent in an architecture comprising of several different agents that collaborate together to detect the hidden information. The basic idea is that, the various Audio Quality Metrics (AQMs) calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are statistically different. GA employs these AQMs to steganalyse the audio data. The overall agent architecture will operate as an Automatic Target Detection (ATD) system. The architecture of ATD system is presented in this paper and it is shown how the Detection Agent fits into the overall system. The design of ATD based audio steganalyzer relies on the choice of these audio quality measures and the construction of a GA based rule generator, which spawns a set of rules that discriminates between the adulterated and the untouched audio samples. Experimental results show that the proposed technique provides promising detection rates.