Dynamics-Level Watermarking of Flow Matching Models with Random Codes
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918504586805248 |
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| author | Wang, Shuchan |
| author_facet | Wang, Shuchan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16239 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamics-Level Watermarking of Flow Matching Models with Random Codes Wang, Shuchan Machine Learning 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. |
| title | Dynamics-Level Watermarking of Flow Matching Models with Random Codes |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.16239 |