InSeC: Steganalysis Model Based on Inter-Codeword Sensitivity Caption for Compressed Speech Streams

Recently, steganalysis of Voice over Internet Protocol (VoIP) compressed speech has gained attention. In real voice communication, Joint Parallel Steganography (JPS) often occurs, where multiple steganography algorithms coexist. The multifaceted nature of JPS, incorporating various steganographic algorithms, poses significant challenges in steganalysis. We believe that detecting JPS accurately requires multi-stage feature extraction, as a single-stage approach fails to yield satisfactory results. In this paper, we propose an efficient steganalysis model based on Inter-codeword Sensitivity Caption, termed InSeC. It consists of two neural modules: the steganography-sensitive codeword-pair caption module, which analyzes changes in codeword pairs before and after modification from multiple perspectives and aggregates these features, and the fine-grained correlation re-perception module, which re-evaluates features within a local range. Our approach improved detection precision by 25.27%, 11.57%, 9.07%, and 9.28% compared to four recent comparison methods on the JPS detection task. The source code for this work is publicly available on https://github.com/zhousandeqingshu/ZhCode.