EKILA: Synthetic Media Provenance and Attribution for Generative Art
EKILA: Synthetic Media Provenance and Attribution for Generative Art
复制标题
DOI：
10.1109/cvprw59228.2023.00098
复制
发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Kar Balan;S. Agarwal;S. Jenni;Andy Parsons;Andrew Gilbert;J. Collomosse
中科院分区：
文献类型：
--
作者：
Kar Balan;S. Agarwal;S. Jenni;Andy Parsons;Andrew Gilbert;J. Collomosse
EKILA是一个去中心化的框架，使创意人员能够因其对生成AI（GenAI）的贡献而获得认可和奖励。EKILA提出了一种强大的视觉归因技术，并将其与新兴的内容来源标准（C2 PA）相结合，以解决合成图像来源的问题-确定负责AI生成图像的生成模型和训练数据。此外，EKILA扩展了不可替代的令牌（NFT）生态系统，引入了权利的令牌化表示，从而实现了资产所有权、权利和归属（ORA）之间的三角关系。利用ORA关系，创作者可以通过我们的归因模型表达对培训的代理同意，并获得分摊的信贷，包括使用其在GenAI中的资产的版税。
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.