基于分层强化学习的生成式艺术模型版权感知激励方案
Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
AI总结：
针对扩散模型生成艺术中的版权侵权问题，提出基于版权度量、TRAK贡献估计和分层强化学习预算分配的新方法，在三个数据集上优于八个基准，实现版权感知的贡献者激励与补偿。
摘要：
使用扩散模型的生成式艺术在图像生成和文本到图像任务中取得了显著性能。然而，生成式艺术对训练数据日益增长的需求引发了关于版权侵权的重大担忧，因为模型可能生成与受版权保护作品高度相似的图像。现有解决方案试图通过扰动扩散模型来降低生成此类图像的可能性，但这往往以牺牲模型性能为代价。另一种方法侧重于对数据持有者的贡献进行经济补偿，但未能充分解决版权损失问题。我们的方法首先引入了一种基于版权法和侵权判例的新颖版权度量。随后，我们采用TRAK方法来估计数据持有者的贡献。为适应持续的数据收集过程，我们将训练分为多轮。最后，我们设计了一种基于强化学习的分层预算分配方法，以确定每轮的预算，并根据数据持有者在每轮中的贡献和版权损失来确定其报酬。在三个数据集上的大量实验表明，我们的方法优于所有八个基准，证明了其在以版权感知方式优化预算分配方面的有效性。据我们所知，这是首个通过补偿贡献者来激励他们并保护其版权的技术工作。
英文摘要：
Generative art using Diffusion models has achieved remarkable performance in image generation and text-to-image tasks. However, the increasing demand for training data in generative art raises significant concerns about copyright infringement, as models can produce images highly similar to copyrighted works. Existing solutions attempt to mitigate this by perturbing Diffusion models to reduce the likelihood of generating such images, but this often compromises model performance. Another approach focuses on economically compensating data holders for their contributions, yet it fails to address copyright loss adequately. Our approach begin with the introduction of a novel copyright metric grounded in copyright law and court precedents on infringement. We then employ the TRAK method to estimate the contribution of data holders. To accommodate the continuous data collection process, we divide the training into multiple rounds. Finally, We designed a hierarchical budget allocation method based on reinforcement learning to determine the budget for each round and the remuneration of the data holder based on the data holder's contribution and copyright loss in each round. Extensive experiments across three datasets show that our method outperforms all eight benchmarks, demonstrating its effectiveness in optimizing budget distribution in a copyright-aware manner. To the best of our knowledge, this is the first technical work that introduces to incentive contributors and protect their copyrights by compensating them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
