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

Abstract

Abstract. 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 highthroughput 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 x which could potentially be a stego-audio object, we consider its de-noised version x as an estimate of the cover-object. We then use Hausdorff distance to measure the distortion from x to x . 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.

A Robust Audio Watermarking Scheme using Higher-order Statistics in Empirical Mode Decomposition Domain

“…In this paper, we will embed digital watermark into audio signal by utilizing the higher-order statistics. The higher order statistics can be obtained in wavelet domain by two steps [27,28]: Hausdorff distance estimation, and higher-order statistics computation.…”

“…Since the distortion metric should be sensitive to the presence of the watermark and its reaction should be proportional to the embedding strength, Hausdorff distance [27,28] is used as a dissimilarity measure. Among dissimilarity measures over binary images, the Hausdorff distance has often been used in the content-based retrieval domain and is known to have successful applications in object matching.…”

“…Among dissimilarity measures over binary images, the Hausdorff distance has often been used in the content-based retrieval domain and is known to have successful applications in object matching. On the other hand, Hausdorff distance is very sensitive to noise [27,28]. A small distortion can result in a significant distance between two objects.…”

“…The higherorder statistics is an effective tool for analyzing multichannel signals, it is not only capable of retaining phase information, but also can suppress Gaussian white noise and Gaussian colored noise. A number of prior studies have shown that higher-order statistics are very effective in image steganalysis and watermarking [26,27]. Although most image steganalysis and watermarking algorithms claim that they can be easily extended to other types of media files (e.g., digital audio), many of the models capture statistical regularities inherent to the spatial composition of images, which are not present in other types of media files such as audio.…”

Multi-Layer Architecture for Efficient Steganalysis of UnderMp3Cover in Multi-Encoder Scenario

“…First, audio quality metrics (AQM) were used for differentiating between covers and stegos [5]. Another work argued that AQM are not suitable for steganalysis and instead used Hausdorff distance [6]. Work of [7] showed that features extracted from second order derivative of signals are more significant.…”