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

2024-08-29

arXiv.org

AI Summary

To address copyright infringement risks in text-to-image diffusion models, this paper proposes a reinforcement learning (RL)-based copyright protection method. The approach fine-tunes diffusion models via RL—specifically, using the DDPO framework—with a novel reward function that incorporates a computationally grounded copyright metric, jointly derived from statutory copyright law and judicial precedents. To ensure training stability, KL divergence regularization is integrated into the RL objective. Experimental results across three mixed copyright/non-copyright datasets demonstrate that the method significantly reduces the probability of generating infringing content while preserving generation quality, as evidenced by stable FID and CLIP Score metrics. Our contributions include: (1) the first formal, executable copyright metric for generative models, seamlessly embedded into RL-based diffusion optimization; and (2) a robust, deployable framework for AIGC copyright governance—quantifiable, optimizable, and production-ready.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The increasing sophistication of text-to-image generative models has led to complex challenges in defining and enforcing copyright infringement criteria and protection. Existing methods, such as watermarking and dataset deduplication, fail to provide comprehensive solutions due to the lack of standardized metrics and the inherent complexity of addressing copyright infringement in diffusion models. To deal with these challenges, we propose a Reinforcement Learning-based Copyright Protection(RLCP) method for Text-to-Image Diffusion Model, which minimizes the generation of copyright-infringing content while maintaining the quality of the model-generated dataset. Our approach begins with the introduction of a novel copyright metric grounded in copyright law and court precedents on infringement. We then utilize the Denoising Diffusion Policy Optimization (DDPO) framework to guide the model through a multi-step decision-making process, optimizing it using a reward function that incorporates our proposed copyright metric. Additionally, we employ KL divergence as a regularization term to mitigate some failure modes and stabilize RL fine-tuning. Experiments conducted on 3 mixed datasets of copyright and non-copyright images demonstrate that our approach significantly reduces copyright infringement risk while maintaining image quality.

Problem

• Image Copyright Infringement
• Watermarking Techniques
• Duplicate Image Removal

Innovation

• Reinforcement Learning
• Copyright Protection
• KL Divergence Adjustment