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.

翻译：为检测复杂场景中的隐写文本，研究者提出了多种动机驱动的语言隐写分析方法并取得了优异性能。然而随着生成式隐写技术的发展，部分隐写文本具有极强的隐蔽性，特别是在基于大型语言模型的隐写方法出现后，现有语言隐写分析方法的检测率较低甚至无法有效检测。我们设计了一种新型双模式语言隐写分析方法LSGC。在生成模式下，我们构建了语言隐写分析任务“描述”，利用大型语言模型的生成能力解释待检测文本是否为隐写文本。在此基础上，我们重新思考了语言隐写分析与大型语言模型的原理，提出了分类模式。在该模式下，LSGC删除了语言隐写分析任务“描述”，采用“因果语言模型”架构的大型语言模型提取隐写特征。仅需单次模型前向传播即可提取隐写特征，并通过添加带初始化权重的线性层获得分类概率。在强隐蔽隐写文本上的实验表明，LSGC显著提升了检测性能并达到当前最优水平。此外，分类模式下的LSGC在保持高性能的同时大幅减少了训练时间。