No abstract

Currently, steganography based on compressed speech streams is gathering more and more attention. Meanwhile, it poses a huge threat to cyber security. As a counter technique, steganalysis can detect whether an illegal secret message is embedded in a compressed speech. To further improve the detection performance of current methods, a novel steganalysis method based on codeword association rule mining (CARM) is proposed in this paper. Firstly, we analyzed the spatiotemporal relationships between codewords in compressed speech. Secondly, the steganography-sensitivity codeword association rule base in training set was built based on the confidence change of codeword association rules before and after steganography. Thirdly, the steganography characteristic index and the corresponding dynamic partition threshold in validation set were computed to determine whether the compressed speech segment contains covert communication or not. Finally, comprehensive experiments were conducted to evaluate the performance of the proposed CARM steganalysis method under various conditions, including different association rule patterns, whether to use dynamic partition threshold, different embedding rates, different speech lengths, etc. The experimental results verify that CARM can achieve better performance than the comparison methods. In addition, the detection accuracy of CARM method can be improved significantly by using dynamic partition threshold at low embedding rates.

Most of the existing steganalysis methods are designed for specific steganography methods in low-bit-rate compressed speech stream and lack of generalization ability. In practical applications, the steganography methods in compressed speech are various and cannot be predicted in advance. We can only employ numerous possible steganalysis method to detect, which is laborious and time-consuming, and cannot achieve real-time detection. Therefore, it is necessary to develop a general steganalysis method that can detect multiple steganography methods simultaneously for compressed speech. To this end, we propose a steganalysis method based on global and local correlation mining in this paper. Firstly, we introduce a codeword distributed embedding module to transform the compressed codewords into a compact feature representation. Then, we propose global-guided correlation mining module and local-guided correlation mining module to extract the correlation change before and after steganography in the view of global and local. Finally, the detection results can be obtained by the full connection layers. Experimental results show that the proposed method can reach a better detection performance than the existing steganalysis methods at different embedding rates and speech lengths.