Papers › Perfectly Secure Steganography Using Minimum Entropy Coupling

Perfectly Secure Steganography Using Minimum Entropy Coupling

24 Oct 2022arXiv:2210.14889archive 2025-07-28

Christian Schroeder de Witt, Samuel Sokota, J. Zico Kolter, Jakob Foerster, Martin Strohmeier

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 Cachin (1998)'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 maximizes information throughput 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 for arbitrary covertext distributions. 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, WaveRNN, and Image Transformer as communication channels. We find that the minimum entropy coupling-based approach achieves 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.

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schroederdewitt/perfectly-secure-steganography officialmentioned in paperpytorch report
ssokota/mec mentioned on GitHubMIT report

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is_probability_zero ssokota/mec/mec/iterative/marginals.py community (archive-listed) ran MIT (permissive) · b68a4b9d3d289c7a · report
entropy ssokota/mec/mec/utilities.py community (archive-listed) unverified MIT (permissive) · 7be456a9b2eae0a8 · report
entropy_upper_bounds ssokota/mec/mec/utilities.py community (archive-listed) unverified MIT (permissive) · ee59a49195ff20ff · report
get_proportional_rows ssokota/mec/mec/utilities.py community (archive-listed) unverified MIT (permissive) · 1eec37a23766f454 · report

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Methods

Absolute Position EncodingsAdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDiscriminative Fine-TuningDropoutGPT-2Label SmoothingLayer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSigmoid ActivationSoftmaxTanh ActivationTransformerWaveRNNWeight Decay

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