通过范围编码实现高效可证明安全的语义隐写术
Efficient Provably Secure Linguistic Steganography via Range Coding
AI总结：
本文提出一种高效且可证明安全的语义隐写术方法，利用范围编码实现高隐写容量和速度，实验显示其在多种语言模型上达到100%的熵利用率。
摘要：
语义隐写术涉及将秘密信息嵌入看似无害的文本中以实现隐秘通信。可证明安全性，长期以来的目标和关键动机，已扩展至基于语言模型的隐写术。先前的可证明安全方法实现了零Kullback-Leibler（KL）发散的完美不可察觉性，但以牺牲嵌入容量为代价。本文尝试直接使用经典熵编码方法（范围编码）实现安全隐写术，并提出一种高效且可证明安全的语义隐写术方法，具有旋转机制。实验表明，本文方法在各种语言模型上实现了约100%的熵利用率（嵌入效率），优于现有基线方法。此外，其嵌入速度高达1554.66 bits/s（在GPT-2上）。代码可在github.com/ryehr/RRC_steganography获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
