Title: Shifting-Merging: Secure, High-Capacity and Efficient Steganography via Large Language Models Show Chinese title

Title: 移位合并：通过大型语言模型实现的安全、高容量和高效隐写术

Abstract: In the face of escalating surveillance and censorship within the cyberspace, the sanctity of personal privacy has come under siege, necessitating the development of steganography, which offers a way to securely hide messages within innocent-looking texts. Previous methods alternate the texts to hide private massages, which is not secure. Large Language Models (LLMs) provide high-quality and explicit distribution, which is an available mathematical tool for secure steganography methods. However, existing attempts fail to achieve high capacity, time efficiency and correctness simultaneously, and their strongly coupling designs leave little room for refining them to achieve better performance. To provide a secure, high-capacity and efficient steganography method, we introduce ShiMer. Specifically, ShiMer pseudorandomly shifts the probability interval of the LLM's distribution to obtain a private distribution, and samples a token according to the private bits. ShiMer produced steganographic texts are indistinguishable in quality from the normal texts directly generated by the language model. To further enhance the capacity of ShiMer, we design a reordering algorithm to minimize the occurrence of interval splitting during decoding phase. Experimental results indicate that our method achieves the highest capacity and efficiency among existing secure steganography techniques. Show Chinese abstract

Abstract: 在面对网络空间中日益加剧的监控和审查时，个人隐私的神圣性受到了威胁，这需要开发隐写术，它提供了一种在看似无害的文本中安全隐藏信息的方法。 以前的方法交替文本以隐藏私人消息，这是不安全的。 大型语言模型（LLMs）提供了高质量且明确的分布，这是用于安全隐写术方法的可用数学工具。 然而，现有的尝试无法同时实现高容量、时间效率和正确性，其强耦合的设计使得进一步优化以获得更好性能的空间有限。 为了提供一种安全、高容量和高效的隐写术方法，我们引入了ShiMer。 具体来说，ShiMer伪随机地移动LLM分布的概率区间以获得私有分布，并根据私有位采样一个标记。 ShiMer生成的隐写文本在质量上与语言模型直接生成的正常文本无法区分。 为了进一步提高ShiMer的容量，我们设计了一个重排序算法，以最小化解码阶段期间区间分割的发生。 实验结果表明，我们的方法在现有安全隐写术技术中实现了最高的容量和效率。