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.

基于大型语言模型用户界面的黑盒生成式文本隐写方法（LLM-Stega）研究了关于大型语言模型的高质量文本生成模糊了人类和机器之间的边界，通过用户界面对 LLM 进行秘密通信。通过构建关键词集合和设计一种新的加密隐写映射算法，提出了一种基于拒绝抽样优化机制来保证秘密信息的准确提取和生成隐写文本丰富语义的 LLM-Stega 方法，全面的实验表明该方法优于当前最先进的方法。