EKILA: Synthetic Media Provenance and Attribution for Generative Art

• URL: http://arxiv.org/abs/2304.04639v1
• Date: Mon, 10 Apr 2023 15:11:26 GMT
• Title: EKILA: Synthetic Media Provenance and Attribution for Generative Art
• Authors: Kar Balan, Shruti Agarwal, Simon Jenni, Andy Parsons, Andrew Gilbert, John Collomosse
• Abstract summary: EKILA is a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI) EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights.
• Score: 18.02055922311379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance -- determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.

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