Linguistic steganography provides convenient implementation to hide messages, particularly with the emergence of AI generation technology. The potential abuse of this technology raises security concerns within societies, calling for powerful linguistic steganalysis to detect carrier containing steganographic messages. Existing methods are limited to finding distribution differences between steganographic texts and normal texts from the aspect of symbolic statistics. However, the distribution differences of both kinds of texts are hard to build precisely, which heavily hurts the detection ability of the existing methods in realistic scenarios. To seek a feasible way to construct practical steganalysis in real world, this paper propose to employ human-like text processing abilities of large language models (LLMs) to realize the difference from the aspect of human perception, addition to traditional statistic aspect. Specifically, we systematically investigate the performance of LLMs in this task by modeling it as a generative paradigm, instead of traditional classification paradigm. Extensive experiment results reveal that generative LLMs exhibit significant advantages in linguistic steganalysis and demonstrate performance trends distinct from traditional approaches. Results also reveal that LLMs outperform existing baselines by a wide margin, and the domain-agnostic ability of LLMs makes it possible to train a generic steganalysis model (Both codes and trained models are openly available in https://github.com/ba0z1/Linguistic-Steganalysis-with-LLMs).

翻译：语言隐写术通过隐藏消息提供了便捷的实现方式，尤其是在AI生成技术兴起的背景下。该技术的潜在滥用引发了社会中的安全担忧，亟需强大的语言隐写分析来检测包含隐写消息的载体。现有方法局限于从符号统计角度发现隐写文本与正常文本之间的分布差异。然而，这两类文本的分布差异难以精确构建，严重损害了现有方法在现实场景中的检测能力。为寻求构建实用隐写分析的可行方案，本文提出利用大语言模型（LLMs）的类人文本处理能力，在传统统计维度之外，从人类感知角度实现差异识别。具体而言，我们通过将任务建模为生成范式而非传统分类范式，系统研究了LLMs在此任务中的表现。大量实验结果表明，生成式LLMs在语言隐写分析中展现出显著优势，并呈现出与传统方法不同的性能趋势。结果还显示，LLMs以较大优势超越现有基准模型，其领域无关能力使得训练通用隐写分析模型成为可能（相关代码与训练模型已在https://github.com/ba0z1/Linguistic-Steganalysis-with-LLMs开源）。