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New watermarking embeds signals in generative model dynamics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel watermarking technique for generative models that embeds signals directly into the learned continuous dynamics, specifically the velocity field of flow matching models. This method formulates watermarking as random coding over a continuous channel, adding a key-dependent perturbation during training that does not alter the generated distribution. Experiments on standard datasets like MNIST and CIFAR-10 demonstrate reliable message recovery and preserved generation quality, with decoding accuracy dropping to chance levels without the secret key. AI

IMPACT Introduces a new method for watermarking generative models, potentially enhancing content provenance and security.

RANK_REASON The cluster contains an academic paper detailing a new technical method for generative models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New watermarking embeds signals in generative model dynamics

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The cluster contains an academic paper detailing a new technical method for generative models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shuchan Wang ·

    Dynamics-Level Watermarking of Flow Matching Models with Random Codes

    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 …

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