Generative Text Steganography with Large Language Model

Jiaxuan Wu, Zhengxian Wu, Yiming Xue, Juan Wen, Wanli Peng

Abstract: 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. Currently, 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 to ensure secure covert communication between Alice (sender) and Bob (receiver) 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, an optimization mechanism based on reject sampling is proposed to guarantee accurate extraction of secret messages and rich semantics of generated stego texts. Comprehensive experiments demonstrate that the proposed LLM-Stega outperforms current state-of-the-art methods.