An improved entropy-based approach to steganalysis of compressed speech

Abstract

Compared with more and more steganography techniques motivated by abundant compressed speech, steganalysis is still a challenging task. Many existing studies are based on a single dimensional feature model and it is difficult to have a wide range of applicability. In this paper, a hybrid Markov model is proposed, which is based on the correlation of fixed codebook parameters in speech codec between pulses in a given track. And then, two detecting methods based on entropy are given. One is designed as a single-pulse position based entropy detection method (SPBE). The other is pulse-position pairs based entropy detection method (PPBE). Simultaneously, to solve the problem of inaccurate calculation of the entropy rate of finite length samples, corrected conditional entropy (CCE) is used as an estimate of the Markov chain entropy rate. Experiments show that CCE and entropy are highly complementary, and both can be employed as classification features to achieve better steganalysis results. Finally, the performance of the proposed detection methods is evaluated and compared with the existing detection methods. Results prove that the two methods proposed in this paper are suitable for online and real-time steganographic detection, especially for small-size samples.