We consider coverless steganography where a Large Language Model (LLM) is used to generate stego-texts in combination with arithmetic coding. An efficient method should embed secret bits in as few language tokens as possible while keeping the stego-text as natural as possible. We show that this problem is equivalent to maximizing the entropy of a replacement probability distribution of the next token generation, subject to a constraint on the divergence between the new distribution and the original one produced by the LLM. A closed-form solution is provided under either the KL divergence or the total variation constraint. Several important practical issues are also tackled: 1) An often-overlooked tokenization mismatch issue is resolved with a simple prompt selection approach, 2) The combination of the optimized distribution and the vocabulary truncation technique is considered, and 3) The incorporation of the proposed approach with existing (potentially non arithmetic coding based) techniques, e.g., the Discop technique.

翻译：本文研究无载体隐写术，其中利用大语言模型结合算术编码生成隐写文本。高效的方法应使用尽可能少的语言符号嵌入秘密比特，同时保持隐写文本尽可能自然。我们证明该问题等价于在满足新分布与LLM生成的原始分布之间散度约束的条件下，最大化下一符号生成替换概率分布的熵。在KL散度或全变差约束下给出了闭式解。同时解决了若干重要实际问题：1）通过简单的提示选择方法解决了常被忽视的符号化失配问题；2）考虑了优化分布与词汇截断技术的结合；3）将所提方法与现有（可能非算术编码基础的）技术（如Discop技术）进行了融合。