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
even 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
changed the "causalLM" LLMs to the "sequenceClassification" architecture. 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.

为了检测复杂场景中的隐写文本，提出了一种名为 LSGC 的新型语言隐写分析方法，其中包括生成模式和分类模式，能够显著提高检测性能并减少训练时间。