Linguistic steganography embeds secret information into seemingly innocuous text to safeguard privacy under surveillance. Generative linguistic steganography leverages the probability distributions of language models (LMs) and applies steganographic algorithms during generation, and has attracted increasing attention with the rise of large language models (LLMs). To strengthen security, prior work has focused on distribution-preserving steganographic algorithms that minimize the gap between stego sampling and random sampling from the model. However, their reliance on model distributions, which often deviate from real-world cover texts, leads to limited imperceptibility when facing steganalysis detectors in practical settings. Moreover, LLM distributions tend to be more deterministic, reducing entropy and thus lowering embedding capacity. In this paper, we propose a plug-and-play method that reconstructs the distributions of language models used for generative linguistic steganography. FreStega dynamically adjusts token probabilities from the language model at each step of autoregressive stego text generation, leveraging both sequential and spatial dimensions. Extensive experiments on four LLMs, three benchmark datasets, and four distribution-preserving steganographic baselines demonstrate that, by reforming the distribution, FreStega improves the imperceptibility of stego text in realistic scenarios and increases steganographic capacity by 15.41\%, without degrading the quality of the generated stegotext.

翻译：语言隐写术将秘密信息嵌入看似无害的文本中，以在监控环境下保护隐私。生成式语言隐写术利用语言模型的概率分布，并在生成过程中应用隐写算法，随着大语言模型的兴起而日益受到关注。为增强安全性，先前工作集中于保持分布的隐写算法，旨在最小化隐写采样与模型随机采样之间的差距。然而，这些方法依赖于模型分布，而模型分布常偏离真实世界的载体文本，导致在实际场景中面对隐写分析检测器时不可感知性有限。此外，大语言模型的分布往往更具确定性，从而降低熵并因此减少嵌入容量。本文提出一种即插即用方法，用于重构生成式语言隐写中所用语言模型的分布。FreStega 在自回归隐写文本生成的每一步中，利用序列和空间维度动态调整来自语言模型的词元概率。在四种大语言模型、三个基准数据集和四种保持分布的隐写基线上的大量实验表明，通过改造分布，FreStega 提升了现实场景中隐写文本的不可感知性，并将隐写容量提高了 15.41\%，且未降低生成隐写文本的质量。