Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models

Diffusion Models (DMs) have shown remarkable capa-bilities in various image-generation tasks. However, there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. To address this issue, we propose a novel framework that em-beds personal watermarks in the generation of adversarial examples. Such examples can force DMs to generate images with visible watermarks and prevent DMs from imitating unauthorized images. We construct a generator based on conditional adversarial networks and design three losses (adversarial loss, GAN loss, and perturbation loss) to gen-erate adversarial examples that have subtle perturbation but can effectively attack DMs to prevent copyright violations. Training a generator for a personal watermark by our method only requires 5–10 samples within 2–3 minutes, and once the generator is trained, it can generate adver-sarial examples with that watermark significantly fast (0.2s per image). We conduct extensive experiments in various conditional image-generation scenarios. Compared to ex-isting methods that generate images with chaotic textures, our method adds visible watermarks on the generated images, which is a more straightforward way to indicate copy-right violations. We also observe that our adversarial exam-ples exhibit good transferability across unknown generative models. Therefore, this work provides a simple yet powerful way to protect copyright from DM-based imitation.