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On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
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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.
Forward citations
Cited by 2 Pith papers
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Authenticated Contradictions from Desynchronized Provenance and Watermarking
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.
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First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge
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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