Steganalysis of Low Bit-Rate Speech Based on Statistic Characteristics of Pulse Positions

Steganography in low bit-rare speech streams is an important branch of Voice-over-IP steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support-vector-machine based steganalysis of low bit-rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long-time distribution feature, extract Markov transition probabilities of pulse positions according to the short-time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse-to-pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a encoded samples, and compare it with the state-of-the-art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods.