RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model

The increasing sophistication of text-to-image generative models raises challenges in defining and enforcing copyright criteria. Existing methods like watermarking and dataset deduplication fall short due to the lack of standardized metrics and the complexity of addressing copyright issues in diffusion models. To tackle these challenges, we propose RLCP, a Reinforcement Learning-based Copyright Protection method for Text-to-Image Diffusion Models. Our approach introduces a novel copyright metric grounded in legal precedents and employs the Denoising Diffusion Policy Optimization (DDPO) framework to minimize copyright-infringing content while preserving image quality. A reward function based on our metric and KL divergence regularization ensures stable fine-tuning. Experiments on mixed datasets of copyright and non-copyright images show that RLCP effectively reduces copyright infringement risk without compromising output quality.