DECORAIT -- DECentralized Opt-in/out Registry for AI Training

TL;DR

DECORAIT is a decentralized registry system that enables content creators to assert rights and receive rewards for their data used in AI training, using blockchain and visual fingerprinting for provenance and consent management.

Contribution

It introduces a scalable, decentralized data governance framework combining DLT and visual fingerprinting to trace AI training data provenance and manage creator consent.

Findings

Prototype demonstrates effective data provenance tracking

Hierarchical clustering improves registry scalability

Integration of C2PA standard enhances security and authenticity

Abstract

We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training as well as receive reward for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data in order to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.