On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915261136764928 |
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| author | Fairoze, Jaiden Ortiz-Jimenez, Guillermo Vecerik, Mel Jha, Somesh Gowal, Sven |
| author_facet | Fairoze, Jaiden Ortiz-Jimenez, Guillermo Vecerik, Mel Jha, Somesh Gowal, Sven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark Fairoze, Jaiden Ortiz-Jimenez, Guillermo Vecerik, Mel Jha, Somesh Gowal, Sven Cryptography and Security Machine Learning 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. |
| title | On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2502.04901 |