Multi-Classification of Linguistic Steganography Driven by Large Language Models

Jie Wang, Yihao Wang, Yihao Li, Ru Zhang, Jianyi Liu

Abstract: In linguistic steganalysis (LS), the fundamental requirement is to detect steganographic text (stego) effectively, and existing LS methods have shown excellent detection performance even in complex scenarios. However, different steganography employs various grammatical rules and syntactic structures, which result in distinct text representations. This diversity complicates the identification of stego in mixed datasets. A major challenge in LS is pinpointing the specific steganography used, which is crucial for developing effective extraction algorithms. Thus, this paper proposes a multi-classification method based on the Large Language Models (LLMs) called LSMC. This approach utilizes the strengths of LLMs in semantic understanding and contextual analysis, allowing for accurate classification. Experimental results show that the LSMC method can efficiently perform multi-classification, precisely discerning the steganography used in mixed datasets with up to 20 categories. This provides a feasible solution for fully deciphering steganography in subsequent stages.