AI Watermarking, Digital Provenance, and FieldID: An Industry and Technical Assessment of Synthetic-Content Trust
Year: 2026
Type: article
Abstract: AI watermarking is moving rapidly from research into production infrastructure. This paper assesses current AI watermarking, digital-provenance, actor-identity, authentication, and transparency mechanisms against the frozen FieldID and CNX architecture. The central argument is that AI participation, provenance, identity, authentication, authority, and factual truth belong to different technical layers. A watermark may provide evidence that a generative system participated in producing content. A provenance manifest may record creation and transformation history. A credential or signature may establish identity or key control. None of these mechanisms independently establishes factual truth or legitimate authority. FieldID is not proposed as a replacement for C2PA, SynthID, CAWG identity assertions, or other provenance standards. Its proposed role is an independent structural-identity substrate within a governed evidence-composition architecture. CNX governs admissibility while downstream reasoning preserves contradiction, uncertainty, recurrence, falsification requirements, and proof burden. The frozen FieldID structural-validity mechanism was internally reproduced with 119 tests passed, 0 failed, and 13 disclosed NumPy RuntimeWarnings that did not produce test failures. This validates the bounded structural-validity mechanism, not the application-specific proposition that seedless AI-generated content produces structural collapse. Seedless AI field collapse, seed persistence through AI-assisted rendering, and recognition-independent seed discrimination remain hypotheses requiring controlled comparative testing. This Zenodo record preserves the CJCI Volume 1, Issue 23, Version 1.2 final frozen article, its supporting-documentation bundle, and the SHA-256 checksum record for that bundle. Originally published by the Carlonoscopen Journal of Coherence Intelligence, Volume 1, Issue 23, on August 12, 2026.
Author: Silva, Ivan
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7202396863