Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques. In this work, we show that a steganography procedure is perfectly secure under \citet{cachin_perfect}'s information theoretic-model of steganography if and only if it is induced by a coupling. Furthermore, we show that, among perfectly secure procedures, a procedure is maximally efficient if and only if it is induced by a minimum entropy coupling. These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees with non-trivial efficiency; additionally, these algorithms are highly scalable. To provide empirical validation, we compare a minimum entropy coupling-based approach to three modern baselines -- arithmetic coding, Meteor, and adaptive dynamic grouping -- using GPT-2 and WaveRNN as communication channels. We find that the minimum entropy coupling-based approach yields superior encoding efficiency, despite its stronger security constraints. In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling.

翻译：隐写术是一种将秘密信息编码到无害内容中，使得对抗性第三方无法意识到存在隐藏意义的实践。虽然该问题在安全文献中已有经典研究，但生成模型的最新进展促使安全与机器学习领域的研究者共同致力于开发可扩展的隐写技术。本研究表明，在Cachin提出的信息论隐写模型下，隐写过程实现完美安全当且仅当它由耦合诱导产生。进一步地，我们证明了在所有完美安全的过程中，达到最高效率当且仅当该过程由最小熵耦合诱导产生。这些洞见催生了据我们所知首批在非平凡效率下实现完美安全保证的隐写算法，且这些算法具有高度可扩展性。为提供经验验证，我们以GPT-2和WaveRNN作为通信通道，将基于最小熵耦合的方法与三种现代基线方法——算术编码、Meteor和自适应动态分组——进行了对比。结果表明，尽管面临更强的安全约束，基于最小熵耦合的方法仍能实现更优的编码效率。综合而言，这些发现提示通过最小熵耦合的视角审视信息论隐写术可能是自然的。