用生成模型去除艺术保护噪声，评估防模仿攻击效果
GLEAN: Generative Learning for Eliminating Adversarial Noise
- 用图像到图像生成网络还原被干扰的画作
- 发现现有保护方法在生成攻击下仍易被破解
- 适合关注数字艺术版权与生成模型安全的研究者
随着DALL-E和Stable Diffusion等强大扩散模型的兴起，许多数字艺术家因作品被微调用于风格模仿攻击而蒙受损失。为防止此类攻击，Glaze工具通过添加扰动保护作品，但会引入从不可察觉噪声到严重质量下降的副作用。本文提出GLEAN——一种基于I2I生成网络的方法，用于从经Glaze处理的图像中去除扰动，并评估在应用GLEAN前后，风格模仿攻击的成功率。实验表明，当前保护手段存在明显漏洞，亟需改进。GLEAN旨在揭示这些局限性，推动更有效的防御机制发展。
原文摘要 · Abstract (English)
In the age of powerful diffusion models such as DALL-E and Stable Diffusion, many in the digital art community have suffered style mimicry attacks due to fine-tuning these models on their works. The ability to mimic an artist's style via text-to-image diffusion models raises serious ethical issues, especially without explicit consent. Glaze, a tool that applies various ranges of perturbations to digital art, has shown significant success in preventing style mimicry attacks, at the cost of artifacts ranging from imperceptible noise to severe quality degradation. The release of Glaze has sparked further discussions regarding the effectiveness of similar protection methods. In this paper, we propose GLEAN- applying I2I generative networks to strip perturbations from Glazed images, evaluating the performance of style mimicry attacks before and after GLEAN on the results of Glaze. GLEAN aims to support and enhance Glaze by highlighting its limitations and encouraging further development.
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