Diffusion Models (DMs) have shown remarkable capabilities 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 embeds 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 generate 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 adversarial examples with that watermark significantly fast (0.2s per image). We conduct extensive experiments in various conditional image-generation scenarios. Compared to existing methods that generate images with chaotic textures, our method adds visible watermarks on the generated images, which is a more straightforward way to indicate copyright violations. We also observe that our adversarial examples exhibit good transferability across unknown generative models. Therefore, this work provides a simple yet powerful way to protect copyright from DM-based imitation.

翻译：扩散模型（DMs）在各类图像生成任务中展现出卓越的能力。然而，越来越多的人担忧DMs可能被用于模仿未经授权的创作，从而引发版权问题。为解决这一问题，我们提出一种新颖框架，通过在对抗样本生成过程中嵌入个人水印。此类样本能够迫使DMs生成带有可见水印的图像，从而阻止DMs模仿未经授权的图像。我们基于条件对抗网络构建生成器，并设计三种损失函数（对抗损失、GAN损失和扰动损失），以生成具有细微扰动但能有效攻击DMs、防止版权侵权的对抗样本。使用我们的方法为个人水印训练生成器仅需5-10个样本和2-3分钟，且生成器训练完成后，可快速生成带有该水印的对抗样本（每张图像0.2秒）。我们在多种条件图像生成场景下进行了广泛实验。相比现有方法生成的图像带有杂乱纹理，我们的方法在生成图像上添加可见水印，这是一种更直观的版权侵权指示方式。我们还观察到，生成的对抗样本在未知生成模型间具有良好的迁移性。因此，本工作提供了一种简单而强大的方法，以保护版权免受基于DMs的模仿侵害。