无需模型分布，仅用API即可实现可证明安全的隐写术
Provable Secure Steganography Based on Adaptive Dynamic Sampling
- 通过动态采样构建消息到令牌的映射，保持原模型分布
- 在三个数据集和三类大模型上达到与现有方法相当的效率和容量
- 适合需要隐蔽通信且无法获取模型分布的场景
隐私通信正面临广泛监控的威胁。隐写术通过将秘密信息嵌入无害载体中，实现对监控通道的隐蔽通信。可证明安全隐写术（PSS）通过确保原始模型输出与隐写输出在计算上不可区分，成为该领域的前沿技术。然而，现有PSS方法通常需获取模型的显式分布。本文提出一种仅需模型API（接收种子输入）的可证明安全隐写方案。核心机制是采样候选令牌集，并构建从消息比特串到这些令牌的映射；实际秘密消息通过此映射选择输出令牌，从而严格保持原始模型分布。为确保正确解码，针对多个消息映射至同一令牌的碰撞情况，维护并动态扩展一个大小受限的碰撞集合。在三个真实世界数据集和三种大型语言模型上的大量实验表明，该采样方法在效率和容量上与现有PSS方法相当。
原文摘要 · Abstract (English)
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.
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