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We present DECORAIT; a decentralized registry through which content creatorsmay assert their right to opt in or out of AI training as well as receivereward for their contributions. Generative AI (GenAI) enables images to besynthesized using AI models trained on vast amounts of data scraped from publicsources. Model and content creators who may wish to share their work openlywithout sanctioning its use for training are thus presented with a datagovernance challenge. Further, establishing the provenance of GenAI trainingdata is important to creatives to ensure fair recognition and reward for theirsuch use. We report a prototype of DECORAIT, which explores hierarchicalclustering and a combination of on/off-chain storage to create a scalabledecentralized registry to trace the provenance of GenAI training data in orderto determine training consent and reward creatives who contribute that data.DECORAIT combines distributed ledger technology (DLT) with visualfingerprinting, leveraging the emerging C2PA (Coalition for Content Provenanceand Authenticity) standard to create a secure, open registry through whichcreatives may express consent and data ownership for GenAI.

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