Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style. To address these emerging copyright violations, in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs. Extensive experiments show that the generated adversarial examples can effectively hinder DMs from extracting their features. Therefore, our method can be a powerful tool for human artists to protect their copyright against infringers equipped with DM-based AI-for-Art applications. The code of our method is available on GitHub: https://github.com/mist-project/mist.git.

翻译：近期，扩散模型（DMs）在AI艺术领域掀起浪潮，却也引发了新的版权担忧——侵权者利用未经授权的绘画作品训练扩散模型，从而生成风格相似的新的绘画作品。为应对这些新兴的版权侵权行为，本文首次探索并提出利用对抗样本来保护人类创作的艺术作品。具体而言，我们首先构建了一个理论框架，用于定义和评估针对扩散模型的对抗样本。随后，基于该框架，我们设计了一种名为AdvDM的新型算法，该算法通过对从扩散模型反向过程中采样的不同潜变量进行优化，利用对抗样本的蒙特卡洛估计来生成扰动。大量实验表明，生成的对抗样本能有效阻碍扩散模型提取其特征。因此，该方法可成为人类艺术家保护其版权、对抗使用基于扩散模型的AI艺术应用的侵权者的有力工具。本方法的代码已在GitHub上开源：https://github.com/mist-project/mist.git。