Audio Steganalysis using Ensemble of Autonomous Multi-Agent and Support Vector Machine Paradigm

This paper investigates the use of support vector machines (SVM) to create and train agents capable of detecting any hidden information in audio files. 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 system exploits a soft computing approach to detect the presence of hidden messages in audio signals, by using the audio quality metrics. The distribution of various statistical distance measures, calculated on cover audio signals and on stego-audio signals vis-a-vis their denoised versions, are different. The overall agent architecture operates 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 SVM classifier, which discriminates between the adulterated and the untouched audio samples