Title: AQM-based audio steganalysis and its realisation through rule induction with a genetic learner
Authors: S. Geetha, Siva S. Sivatha Sindhu, N. Kamaraj
Addresses: Department of Information Technology, Thiagarajar College of Engineering, Madurai-625 015, Tamil Nadu, India. ' Department of Information Technology, Thiagarajar College of Engineering, Madurai-625 015, Tamil Nadu, India. ' Department of Electrical and Electronic Engineering, Thiagarajar College of Engineering, Madurai-625 015, Tamil Nadu, India
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.
Keywords: audio steganalysis; audio quality metrics; denoising; GAs; genetic algorithms; information security; steganography; covert communication.
International Journal of Signal and Imaging Systems Engineering, 2008 Vol.1 No.2, pp.99 - 107
Published online: 24 Oct 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article