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

Kar Balan[1], Alex Black[1], Simon Jenni[2], Andy Parsons [2], Andrew Gilbert[1], John Collomosse[1,2]
Unviersity of Surrey[1], Adobe Resarch [2]
​​The 20th ACM SIGGRAPH European Conference on Visual Media Production
Paper

The DECORAIT and Dreambooth pipeline including registry querying and model personalization flow. The Dreambooth model is specialized using the 3 opted-in images of a car and the proposed apportionment algorithm is applied across the image corpus. The red cross indicated images which have been opted-out according to the DECORAIT registry. The resulting apportionment conducted on the generated synthetic image from the experiment as described in Sec.\ref{eval_dreambooth} is shown. The DLT wallet addresses of the three authors of the images are identified using the accompanying C2PA manifests. Payment is then conducted automatically, securely, and transparently using DLT, and one transaction's confirmation is pictured.

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.

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BibTeX

@inproceedings{Balan:CVMP:2023,
        AUTHOR = Balan, Kar and Black, Alex and Jenni, Simon  and Parsons, Andy and  Gilbert Andrew and Collomosse, John",
        TITLE = "DECORAIT - DECentralized Opt-in/out Registry for AI Training​",
        BOOKTITLE = " The 20th ACM SIGGRAPH European Conference on Visual Media Production",
        YEAR = "2023",
        ​}