Watermarking Diffusion Models By Constructing Generative Classifiers

Diffusion models have demonstrated outstanding performance in generating photorealistic images, garnering significant interest for their application in a wide range of downstream tasks. However, their powerful generative capabilities and increasing deployment raise serious concerns regarding copyright protection. While watermarking techniques for diffusion models have been proposed to address potential misuse, ensuring robustness against model weight modifications remains a persistent challenge. In this paper, we propose a robust watermarking method for diffusion models. Our approach leverages the diffusion model as a generative prior and constructs a generative classifier to facilitate an effective watermarking scheme. Drawing inspiration from fragile watermarking techniques for neural network classifiers—where trigger images are optimized to make predictions highly sensitive to weight changes—we invert this idea to enhance robustness. Specifically, we employ a bi-level optimization framework that jointly optimizes the trigger images and the parameters of the diffusion model. Experiments demonstrate the effectiveness of our proposed method in terms of watermark extraction accuracy, robustness, and model fidelity.