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On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark

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arxiv 2502.04901 v2 pith:VIWVKJ5O submitted 2025-02-07 cs.CR cs.LG

classification cs.CRcs.LG
keywords robustschemeprovenancepublic-detectabilitypublicly-detectableunforgeabilityworkaddressed
verification ladder T0 review T1 audit T2 compute T3 formal
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This work investigates the theoretical boundaries of creating publicly-detectable schemes to enable the provenance of watermarked imagery. Metadata-based approaches like C2PA provide unforgeability and public-detectability. ML techniques offer robust retrieval and watermarking. However, no existing scheme combines robustness, unforgeability, and public-detectability. In this work, we formally define such a scheme and establish its existence. Although theoretically possible, we find that at present, it is intractable to build certain components of our scheme without a leap in deep learning capabilities. We analyze these limitations and propose research directions that need to be addressed before we can practically realize robust and publicly-verifiable provenance.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Authenticated Contradictions from Desynchronized Provenance and Watermarking

    cs.CR 2026-03 unverdicted novelty 6.0 of 10

    C2PA manifests and AI watermarks can independently validate contradictory claims on the same asset, and a cross-layer audit protocol resolves this with 100% accuracy on 3500 images.

  2. First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A competition-winning pipeline removes 95.7% of StegaStamp and TreeRing watermarks on the NeurIPS 2024 benchmark by combining VAE fine-tuning, diffusion purification, and translation tricks.

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