Recent advances in large language models (LLMs) have blurred the boundary of high-quality text generation between humans and machines, which is favorable for generative text steganography. While, current advanced steganographic mapping is not suitable for LLMs since most users are restricted to accessing only the black-box API or user interface of the LLMs, thereby lacking access to the training vocabulary and its sampling probabilities. In this paper, we explore a black-box generative text steganographic method based on the user interfaces of large language models, which is called LLM-Stega. The main goal of LLM-Stega is that the secure covert communication between Alice (sender) and Bob (receiver) is conducted by using the user interfaces of LLMs. Specifically, We first construct a keyword set and design a new encrypted steganographic mapping to embed secret messages. Furthermore, to guarantee accurate extraction of secret messages and rich semantics of generated stego texts, an optimization mechanism based on reject sampling is proposed. Comprehensive experiments demonstrate that the proposed LLM-Stega outperforms current state-of-the-art methods.

翻译：近年来，大语言模型（LLMs）的进展模糊了人类与机器之间高质量文本生成的界限，这为生成式文本隐写提供了有利条件。然而，当前先进的隐写映射方法并不适用于LLMs，因为大多数用户仅限于访问LLMs的黑盒API或用户界面，从而无法获取训练词汇表及其采样概率。本文探索了一种基于大语言模型用户界面的黑盒生成式文本隐写方法，称为LLM-Stega。LLM-Stega的主要目标是使Alice（发送方）与Bob（接收方）能够通过LLMs的用户界面进行安全的隐蔽通信。具体而言，我们首先构建一个关键词集并设计一种新的加密隐写映射来嵌入秘密信息。此外，为保证秘密信息的准确提取和生成隐写文本的丰富语义，提出了一种基于拒绝采样的优化机制。综合实验表明，所提出的LLM-Stega方法优于当前最先进的方法。