Steganalysis of Compressed Speech Based on Global and Local Correlation Mining

Abstract

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, a steganalysis method based on global and local correlation mining is proposed in this paper. Firstly, a codeword distributed embedding module is introduced to transform the compressed codewords into a compact feature representation. Then, global-guided correlation mining module and local-guided correlation mining module are used 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.