UnlearnCanvas：用于增强扩散模型中机器遗忘评估的风格化图像数据集
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
AI总结：
针对现有扩散模型机器遗忘评估体系的缺陷，研究提出高分辨率风格化图像数据集UnlearnCanvas，构建含7项指标的标准化评估框架，基准测试9种前沿方法并探索高难度遗忘场景，以推动更鲁棒的遗忘方法开发。
摘要：
扩散模型（DMs）的技术进步在文本到图像生成中展现出前所未有的能力，被广泛应用于各类场景。但它们也引发了重大的社会担忧，比如有害内容生成和版权纠纷。机器遗忘（MU）已成为一种有前景的解决方案，能够从DMs中移除不需要的生成能力。然而，现有的MU评估体系存在若干关键挑战，可能导致评估不完整且不准确。为解决这些问题，我们提出UnlearnCanvas，这是一个全面的高分辨率风格化图像数据集，可用于评估艺术风格及相关对象的遗忘效果。该数据集支持构建标准化的自动化评估框架，包含7项量化指标，用于评估DMs遗忘性能的各个方面。通过大量实验，我们对9种最先进的DMs MU方法进行了基准测试，揭示了关于其优势、劣势及底层机制的新见解。此外，我们探索了DMs的高难度遗忘场景，以评估其在对抗性提示、更细粒度概念遗忘和序列遗忘下的最坏情况性能。我们希望本研究能为开发更有效、准确、鲁棒的DM遗忘方法铺平道路，确保未来DMs更安全、更符合伦理的应用。数据集、基准测试和代码已在https://unlearn-canvas.netlify.app/ 公开。
英文摘要：
The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. However, they have also raised significant societal concerns, such as the generation of harmful content and copyright disputes. Machine unlearning (MU) has emerged as a promising solution, capable of removing undesired generative capabilities from DMs. However, existing MU evaluation systems present several key challenges that can result in incomplete and inaccurate assessments. To address these issues, we propose UnlearnCanvas, a comprehensive high-resolution stylized image dataset that facilitates the evaluation of the unlearning of artistic styles and associated objects. This dataset enables the establishment of a standardized, automated evaluation framework with 7 quantitative metrics assessing various aspects of the unlearning performance for DMs. Through extensive experiments, we benchmark 9 state-of-the-art MU methods for DMs, revealing novel insights into their strengths, weaknesses, and underlying mechanisms. Additionally, we explore challenging unlearning scenarios for DMs to evaluate worst-case performance against adversarial prompts, the unlearning of finer-scale concepts, and sequential unlearning. We hope that this study can pave the way for developing more effective, accurate, and robust DM unlearning methods, ensuring safer and more ethical applications of DMs in the future. The dataset, benchmark, and codes are publicly available at https://unlearn-canvas.netlify.app/.
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。
