General Steganalysis of Generative Linguistic Steganography Based on Dynamic Segment-Level Lexical Association Extraction

In scenarios where steganographic texts from various steganographic domains generated by different generative steganography algorithms are mixed, most existing linguistic steganalysis methods lack corresponding network structures designed to account for the differences in steganographic texts from different domains, leading to the potential for further improvement in their general detection performance. To address the above issue, we propose a general generative linguistic steganalysis method based on the basic idea of dynamically extracting lexical association features of different steganographic domains at the segment level. We utilize dynamic-static text feature matrix to construct a word importance semantic encoding module to mine steganography-sensitive word features of different steganographic domains. Based on the obtained features, we propose a word correlation multi-scale perception module to focus on the segment-level lexical association changes caused by secret information embedding in different domains. Experimental results show that this method can improve the detection accuracy of existing mainstream linguistic steganalysis methods in various mixed steganography scenarios.