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 have blurred the boundary of high-quality text generation between humans and machines%2C which is favorable for generative text steganography. While%2C 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%2C thereby lacking access to the training vocabulary and its sampling probabilities. In this paper%2C we explore a black-box generative text steganographic method based on the user interfaces of large language models%2C 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%2C We first construct a keyword set and design a new encrypted steganographic mapping to embed secret messages. Furthermore%2C to guarantee accurate extraction of secret messages and rich semantics of generated stego texts%2C an optimization mechanism based on reject sampling is proposed. Comprehensive experiments demonstrate that the proposed LLM-Stega outperforms current state-of-the-art methods.?quality=80&w=800)
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