Abstract
Figure 1: DECORAIT enables creatives to register consent (or not) for Generative AI training using their content, as well as to receive recognition and reward for that use. Provenance is traced via visual matching, and consent and ownership registered using a distributed ledger (blockchain). Here, a synthetic image is generated via the Dreambooth[32] method using prompt "a photo of [Subject]" and concept images (left). The red cross indicates images whose creatives have opted out of AI training via DECORAIT, which when taken into account leads to a significant visual change (right). DECORAIT also determines credit apportionment across the opted-in images and pays a proportionate reward to creators via crypto-currency micropyament. 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 gov-ernance 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 DECO-RAIT, 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.