























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.
| Comments: | 18 pages, 3 figures, code available at: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16239 [cs.LG] |
| (or arXiv:2605.16239v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16239 arXiv-issued DOI via DataCite (pending registration) |
From: Shuchan Wang [view email]
[v1]
Fri, 15 May 2026 17:48:22 UTC (422 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。