Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is a long-standing goal and key motivation, has been extended to language-model-based steganography. Previous provably secure approaches have achieved perfect imperceptibility, measured by zero Kullback-Leibler (KL) divergence, but at the expense of embedding capacity. In this paper, we attempt to directly use a classic entropy coding method (range coding) to achieve secure steganography, and then propose an efficient and provably secure linguistic steganographic method with a rotation mechanism. Experiments across various language models show that our method achieves around 100% entropy utilization (embedding efficiency) for embedding capacity, outperforming the existing baseline methods. Moreover, it achieves high embedding speeds (up to 1554.66 bits/s on GPT-2). The code is available at github.com/ryehr/RRC_steganography.

翻译：语言隐写术涉及将秘密信息嵌入看似无害的文本中，以实现隐蔽通信。作为长期追求的目标和关键动机，可证明安全性已被扩展至基于语言模型的隐写术。以往的可证明安全方法通过零KL散度实现了完美不可感知性，但牺牲了嵌入容量。本文尝试直接使用经典熵编码方法（区间编码）实现安全隐写，进而提出一种结合旋转机制的高效可证明安全语言隐写方法。在多种语言模型上的实验表明，本方法在嵌入容量上实现了接近100%的熵利用率（嵌入效率），优于现有基线方法。此外，其实现了高嵌入速率（在GPT-2上可达1554.66比特/秒）。代码开源在github.com/ryehr/RRC_steganography。