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显著威胁。具体而言，模型可通过在艺术家作品的样本上进行“微调”来学习模仿特定艺术家的艺术风格。本文描述了Glaze的设计、实现与评估——该工具使艺术家能够在线上分享作品前对其应用“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。我们与专业艺术家群体协作，对超过1000名艺术家开展了用户研究，评估他们对AI艺术的态度、工具的效果、可用性与扰动可容忍度，以及在不同场景和针对适应性对抗措施下的鲁棒性。调查艺术家及基于CLIP的实证评分均表明，即使在低扰动水平（p=0.05）下，Glaze在常规条件下（>92%）和面对适应性对抗措施（>85%）时，仍能高效破坏模仿行为。