On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark
It addresses the problem of ensuring robust and verifiable provenance for watermarked imagery, which is incremental as it builds on existing metadata and ML techniques.
This work investigates the theoretical boundaries of creating publicly-detectable watermarking schemes for image provenance, establishing their existence but finding current practical construction intractable due to limitations in deep learning capabilities.
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.