Steganalysis of Compressed Speech Based on Markov and Entropy

Compressed domain based steganography (CDBS) is a kind of relatively new and secure audio steganography. Up to date, there is little research on the steganalysis against this kind of audio steganography. In this paper, we introduce two methods to detect various CDBS on ACELP speech. One is the Markov method and the other is the entropy method. Both methods are based on the observation that the steganography behavior has certain effects on the relationship among the pulses in the same track. The Markov transition probabilities are utilized to evaluate the interrelationships between adjacent pulses and entropy is employed to measure the “disorder” of combined pulse distributions. First, Markov transition probabilities, joint entropy and conditional entropy features of the track pulses are extracted; a support vector machine (SVM) is then applied to the features for discovering the existence of hidden data in compressed speech signals, respectively. Some famous CDBS methods on ACELP encoded speech are considered. Experimental results have proven the effectiveness of the two methods.