With the popularity of the large language models (LLMs), text steganography has achieved remarkable performance. However, existing methods still have some issues: (1) For the white-box paradigm, this steganography behavior is prone to exposure due to sharing the off-the-shelf language model between Alice and Bob.(2) For the black-box paradigm, these methods lack flexibility and practicality since Alice and Bob should share the fixed codebook while sharing a specific extracting prompt for each steganographic sentence. In order to improve the security and practicality, we introduce a black-box text steganography with a dynamic codebook and multimodal large language model. Specifically, we first construct a dynamic codebook via some shared session configuration and a multimodal large language model. Then an encrypted steganographic mapping is designed to embed secret messages during the steganographic caption generation. Furthermore, we introduce a feedback optimization mechanism based on reject sampling to ensure accurate extraction of secret messages. Experimental results show that the proposed method outperforms existing white-box text steganography methods in terms of embedding capacity and text quality. Meanwhile, the proposed method has achieved better practicality and flexibility than the existing black-box paradigm in some popular online social networks.

翻译：随着大语言模型（LLMs）的普及，文本隐写术取得了显著性能。然而现有方法仍存在以下问题：（1）在白盒范式中，由于Alice和Bob共享现成语言模型，隐写行为易暴露；（2）在黑盒范式中，Alice和Bob需共享固定码本，并为每个隐写句子共享特定提取提示，导致方法缺乏灵活性和实用性。为提升安全性和实用性，我们提出一种基于动态码本和多模态大语言模型的黑盒文本隐写方法。具体而言：首先通过共享会话配置与多模态大语言模型构建动态码本，然后设计加密隐写映射，在隐写式描述生成过程中嵌入秘密信息。进一步，我们引入基于拒绝采样的反馈优化机制，确保秘密信息的准确提取。实验结果表明，所提方法在嵌入容量和文本质量上均优于现有白盒文本隐写方法，同时在部分主流在线社交网络中展现出比现有黑盒范式更强的实用性和灵活性。