Dynamics-Level Watermarking of Flow Matching Models with Random Codes

Shuchan Wang

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arXiv · 2605.16239

Dynamics-Level Watermarking of Flow Matching Models with Random Codes

Shuchan Wang

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Abstract

We introduce a dynamics-level approach to watermarking generative models. Rather than embedding signals into model weights or outputs, we embed the watermark directly into the learned continuous dynamics -- the velocity field of a flow matching model. We formulate this as random coding over a continuous channel: a key-dependent perturbation is added during training, and the message is recovered at detection time from black-box queries. The perturbation is designed to leave the generated distribution unchanged. Experiments on MNIST and CIFAR-10 across different architectures confirm reliable message recovery, preserved generation quality, and chance-level decoding accuracy without the secret key.

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Shuchan Wang. 2026-05-15. Dynamics-Level Watermarking of Flow Matching Models with Random Codes. https://arxiv.org/abs/2605.16239

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