  To detect stego (steganographic text) in complex scenarios, linguistic
steganalysis (LS) with various motivations has been proposed and achieved
excellent performance. However, with the development of generative
steganography, some stegos have strong concealment, especially after the
emergence of LLMs-based steganography, the existing LS has low detection or
cannot detect them. We designed a novel LS with two modes called LSGC. In the
generation mode, we created an LS-task "description" and used the generation
ability of LLM to explain whether texts to be detected are stegos. On this
basis, we rethought the principle of LS and LLMs, and proposed the
classification mode. In this mode, LSGC deleted the LS-task "description" and
used the "causalLM" LLMs to extract steganographic features. The LS features
can be extracted by only one pass of the model, and a linear layer with
initialization weights is added to obtain the classification probability.
Experiments on strongly concealed stegos show that LSGC significantly improves
detection and reaches SOTA performance. Additionally, LSGC in classification
mode greatly reduces training time while maintaining high performance.
