Generative linguistic steganography attempts to hide secret messages into
covertext. Previous studies have generally focused on the statistical
differences between the covertext and stegotext, however, ill-formed stegotext
can readily be identified by humans. In this paper, we propose a novel
zero-shot approach based on in-context learning for linguistic steganography to
achieve better perceptual and statistical imperceptibility. We also design
several new metrics and reproducible language evaluations to measure the
imperceptibility of the stegotext. Our experimental results indicate that our
method produces $1.926\times$ more innocent and intelligible stegotext than any
other method.

本研究提出了一种基于上下文学习的零样本方法，用于语言隐藏，以更好地实现感知和统计上的不可察觉性，并设计了几个新的度量标准和可复现的语言评估方法来衡量隐藏文本的隐晦性。实验结果表明，该方法产生了比其他任何方法更多的无罪和可理解的隐藏文本。

零封装生成型语言隐写术

Zero-shot Generative Linguistic Steganography

Generative linguistic steganography mainly utilized language models and
applied steganographic sampling (stegosampling) to generate high-security
steganographic text (stegotext). However, previous methods generally lead to
statistical differences between the conditional probability distributions of
stegotext and natural text, which brings about security risks. In this paper,
to further ensure security, we present a novel provably secure generative
linguistic steganographic method ADG, which recursively embeds secret
information by Adaptive Dynamic Grouping of tokens according to their
probability given by an off-the-shelf language model. We not only prove the
security of ADG mathematically, but also conduct extensive experiments on three
public corpora to further verify its imperceptibility. The experimental results
reveal that the proposed method is able to generate stegotext with nearly
perfect security.

本文提出一种名为 ADG 的具有可证明安全性的生成语言隐写方法，该方法使用自适应动态分组的令牌递归嵌入秘密信息以提高安全性。 通过在三个公共语料库上进行广泛实验，证明了 ADG 的不可察觉性并成功生成几乎完美安全的隐写文本。