对抗样本有益：通过对抗样本防止扩散模型模仿绘画
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
AI总结：
针对扩散模型未经授权模仿绘画风格的版权问题，本文提出AdvDM算法，通过在反向过程潜变量上优化并进行蒙特卡洛估计生成对抗样本，有效阻止DM提取艺术图像特征。
摘要：
近来，扩散模型（Diffusion Models, DMs）推动了AI艺术的浪潮，但也引发了新的版权担忧：侵权者利用未经授权的绘画训练DMs，以生成风格相似的新绘画并从中获利。为应对这些新兴的版权侵犯问题，本文首次探索并提出利用面向DMs的对抗样本来保护人类创作的艺术品。具体而言，我们首先构建了一个理论框架，用于定义和评估面向DMs的对抗样本。随后，基于该框架，我们设计了一种名为AdvDM的新算法，该算法通过对从DMs反向过程中采样的不同潜变量进行优化，利用蒙特卡洛方法估计面向DMs的对抗样本。大量实验表明，所生成的对抗样本能够有效阻碍DMs提取其特征。因此，我们的方法可成为人类艺术家保护自身版权、抵御配备基于DM的AI艺术应用的侵权者的有力工具。本方法代码已在GitHub公开：https://github.com/mist-project/mist.git。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
