Generative Text Steganography with Large Language Model
Jiaxuan Wu Zhengxian Wu Yiming Xue Juan Wen Wanli Peng
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.