The security of private communication is increasingly at risk due to widespread surveillance. Steganography, a technique for embedding secret messages within innocuous carriers, enables covert communication over monitored channels. Provably Secure Steganography (PSS), which ensures computational indistinguishability between the normal model output and steganography output, is the state-of-the-art in this field. However, current PSS methods often require obtaining the explicit distributions of the model. In this paper, we propose a provably secure steganography scheme that only requires a model API that accepts a seed as input. Our core mechanism involves sampling a candidate set of tokens and constructing a map from possible message bit strings to these tokens. The output token is selected by applying this mapping to the real secret message, which provably preserves the original model's distribution. To ensure correct decoding, we address collision cases, where multiple candidate messages map to the same token, by maintaining and strategically expanding a dynamic collision set within a bounded size range. Extensive evaluations of three real-world datasets and three large language models demonstrate that our sampling-based method is comparable with existing PSS methods in efficiency and capacity.

翻译：由于广泛存在的监控，私人通信的安全性日益受到威胁。隐写术作为一种将秘密信息嵌入无害载体的技术，能够在受监控的信道上实现隐蔽通信。可证明安全隐写术通过确保正常模型输出与隐写输出在计算上不可区分，代表了该领域的最先进水平。然而，现有的PSS方法通常需要获取模型的显式分布。本文提出一种仅需接受种子作为输入的模型API即可实现的可证明安全隐写方案。我们的核心机制包括采样一个候选标记集，并构建从可能的消息比特串到这些标记的映射。通过将此映射应用于真实秘密消息来选择输出标记，该方法可证明保持了原始模型的分布。为确保正确解码，我们通过维护并在有限大小范围内策略性地扩展动态冲突集，解决了多个候选消息映射到同一标记的冲突情况。对三个真实世界数据集和三个大型语言模型的广泛评估表明，我们基于采样的方法在效率和容量方面与现有PSS方法相当。