A Novel Audio Steganalysis Based on High-Order Statistics of a Distortion Measure with Hausdorff Distance

Steganography can be used to hide information in audio media both for the purposes of digital watermarking and establishing covert communication channels. Digital audio provides a suitable cover for high-throughput steganography as a result of its transient and unpredictable characteristics. Distortion measure plays an important role in audio steganalysis - the analysis and classification method of determining if an audio medium is carrying hidden information. In this paper, we propose a novel distortion metric based on Hausdorff distance. Given an audio object xwhich could potentially be a stego-audio object, we consider its de-noised version x茂戮驴 as an estimate of the cover-object. We then use Hausdorff distance to measure the distortion from xto x茂戮驴. The distortion measurement is obtained at various wavelet decomposition levels from which we derive high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. Extensive experimental results for the Least Significant Bit (LSB) substitution based steganography tool show that the proposed algorithm has a strong discriminatory ability and the performance is significantly superior to existing methods. The proposed approach can be easily applied to other steganography tools and algorithms.