π€ AI Summary
This study addresses the lack of cross-modal unified evaluation and open-source compliance verification for soft binding between watermarking and fingerprinting in content provenance. Under a unified protocol, we benchmark image, audio, and video modalities using strictly auditable open-source models such as PixelSeal. By integrating content fingerprint retrieval, statistical calibration, and source-level bootstrap interval estimation, the proposed framework systematically evaluates perceptual quality, robustness, and false positive rates. This work presents the first cross-modal unified assessment of both technologies, identifying optimal open-source solutions, quantifying failure mode discrepancies under diverse attacks, and revealing the impact of platform color-space conversions on watermark bit stability.
π Abstract
Content-provenance standards such as C2PA let a platform recover a stripped manifest through a soft binding: an invisible watermark read from the content, or a fingerprint looked up in a registry. We benchmarked both families under one protocol, restricted to openly available models whose licences we audited, on public media, with false-match rates calibrated on held-out negatives and source-level bootstrap intervals for performance estimates. For watermarking we evaluated 25 image, 7 audio and 7 video configurations from 12 methods on perceptual quality, robustness, false positives and cost; for fingerprinting, 35 methods on registries of up to 98,985 images, partial edits and adversarial attacks. PixelSeal gave the best balance for image and video watermarks and AudioSeal for audio, but the error-correcting detector of every TrustMark variant fired on 5.9 to 15.3% of unmarked images, so a verifier should test the expected payload. Among fingerprints, copy detectors trained on non-commercial data detected up to 75.5% of transformed images at a pair-level false-match rate of $10^{-7}$ and DINOv2, the best permissively licensed method, 65.0%; near-copies in a product catalogue dominated the false matches, and no detection improvement from geometric verification was observed under a matched calibration false-binding constraint in the evaluated image pipelines. On the same attacked copies the two families failed differently: the union of watermark and fingerprint successes covered 69% of image copies; fingerprints covered more audio queries, whereas expected-key watermark verification covered more video queries. Embedding a watermark moved the ISCC code of 62.0% of images past its match threshold, and platform-dependent colour conversion changed watermark bits between x86 and ARM hosts.