Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mimic the artistic style of specific artists after "fine-tuning" on samples of their art. In this paper, we describe the design, implementation and evaluation of Glaze, a tool that enables artists to apply "style cloaks" to their art before sharing online. These cloaks apply barely perceptible perturbations to images, and when used as training data, mislead generative models that try to mimic a specific artist. In coordination with the professional artist community, we deploy user studies to more than 1000 artists, assessing their views of AI art, as well as the efficacy of our tool, its usability and tolerability of perturbations, and robustness across different scenarios and against adaptive countermeasures. Both surveyed artists and empirical CLIP-based scores show that even at low perturbation levels (p=0.05), Glaze is highly successful at disrupting mimicry under normal conditions (>92%) and against adaptive countermeasures (>85%).

翻译：近期诸如MidJourney和Stable Diffusion等文本到图像扩散模型，对专业艺术家群体构成了取代威胁。具体而言，这些模型能通过对艺术家作品的样本进行"微调"来学习模仿特定艺术风格。本文描述了Glaze工具的设计、实现与评估，该工具使艺术家能在分享作品前对其施加"风格伪装"。这些伪装对图像施加几乎不可察觉的扰动，当被用作训练数据时，能误导试图模仿特定艺术家的生成模型。我们与专业艺术家社群协作，开展了涵盖逾1000名艺术家的用户研究，评估他们对AI艺术的看法、工具的有效性、可用性及扰动容忍度，以及在不同场景下和针对适应性对抗措施的鲁棒性。接受调查的艺术家以及基于CLIP的实证评分均显示：即使在低扰动水平（p=0.05）下，Glaze在正常条件下（>92%）以及针对适应性对抗措施（>85%）均能高效遏制风格模仿。