Random Coding

Dynamics-Level Watermarking of Flow Matching Models with Random Codes

This paper proposes embedding watermarks directly into the velocity field (continuous dynamics) of flow matching generative models, rather than into weights or outputs. The method uses key-dependent perturbations added during training, formulated as random coding over a continuous channel, allowing black-box message recovery at detection time. The perturbation is designed to leave the generated distribution unchanged. Experiments on MNIST and CIFAR-10 demonstrate reliable message recovery, preserved generation quality, and chance-level decoding without the secret key.