Dynamics-Level Watermarking of Flow Matching Models with Random Codes

Researchers publishing on ArXiv have proposed a watermarking technique for flow matching generative models that embeds identifiers at the dynamics level during the generation process, rather than modifying outputs after the fact.

The method uses random codes to encode provenance information directly into the model's underlying computational dynamics. According to the paper, this approach is designed to resist common watermark removal techniques that typically target output-level modifications — such as image cropping, noise addition, or style transfer.

Flow matching models are a class of generative architecture increasingly used in image and video synthesis. Post-hoc watermarking on these systems has been vulnerable to removal because the identifier sits outside the core generation process.

The proposed technique is framed around two practical use cases: model provenance verification and IP protection for licensed generative AI deployments. The paper does not specify production-ready implementation timelines or report results against adversarial removal attacks beyond standard benchmarks described in the preprint.

Builders licensing or distributing flow matching models to third parties should evaluate whether dynamics-level embedding provides stronger provenance guarantees than their current output-level approaches, particularly in contexts where downstream users have API-level or white-box access to the model.