The paper "Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models" addresses the issue of copyright violations caused by diffusion models (DMs) generating images that imitate unauthorized creations. To prevent this, the authors propose a novel framework that embeds personal watermarks into the generation of adversarial examples. These examples force DMs to produce images with visible watermarks, making it easier to trace copyright ownership. The method uses a conditional GAN architecture with three losses: adversarial loss, GAN loss, and perturbation loss, to generate adversarial examples that are subtle but effective in attacking DMs. The generator can be trained with only 5-10 samples in 2-3 minutes and generates adversarial examples at a speed of 0.2 seconds per image. Extensive experiments in various image-generation scenarios demonstrate the effectiveness and robustness of the method, showing that it can prevent unauthorized images from being learned and generate visible watermarks. The method also exhibits good transferability across different generative models, making it a powerful tool for protecting copyrighted images from imitation by DMs.The paper "Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models" addresses the issue of copyright violations caused by diffusion models (DMs) generating images that imitate unauthorized creations. To prevent this, the authors propose a novel framework that embeds personal watermarks into the generation of adversarial examples. These examples force DMs to produce images with visible watermarks, making it easier to trace copyright ownership. The method uses a conditional GAN architecture with three losses: adversarial loss, GAN loss, and perturbation loss, to generate adversarial examples that are subtle but effective in attacking DMs. The generator can be trained with only 5-10 samples in 2-3 minutes and generates adversarial examples at a speed of 0.2 seconds per image. Extensive experiments in various image-generation scenarios demonstrate the effectiveness and robustness of the method, showing that it can prevent unauthorized images from being learned and generate visible watermarks. The method also exhibits good transferability across different generative models, making it a powerful tool for protecting copyrighted images from imitation by DMs.