EKILA: Synthetic Media Provenance and Attribution for Generative Art

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.

1. Introduction

EKILA combines robust visual attribution with Distributed Ledger Technology (DLT) to recognize and reward creative contributions to generative art. A cymbal generated by a Latent Diffusion Model (LDM) trained on LAION-400M is attributed to a subset of training images, credit weight apportioned, and royalties paid using our proposed method.