标题： AdvAnchor：使用对抗锚点增强扩散模型的遗忘能力 显示英文标题

标题： AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors

摘要： 围绕文本到图像扩散模型的安全问题，研究者通过微调来去除不当概念。最近的微调方法通常将不安全提示的预测分布与预定义的文本锚点对齐。然而，这些技术在消除不良概念和保留其他概念之间表现出显著的性能权衡。本文系统地分析了不同文本锚点对去学习性能的影响。基于此分析，我们提出了AdvAnchor，这是一种新颖的方法，通过生成对抗性锚点来缓解这种权衡问题。这些对抗性锚点被精心设计，以紧密匹配不良概念的嵌入表示，从而维持整体模型性能，同时选择性地排除这些概念的定义属性以实现有效擦除。大量实验表明，AdvAnchor优于现有最先进的方法。我们的代码已公开发布于 https://anonymous.4open.science/r/AdvAnchor。 显示英文摘要

摘要： Security concerns surrounding text-to-image diffusion models have driven researchers to unlearn inappropriate concepts through fine-tuning. Recent fine-tuning methods typically align the prediction distributions of unsafe prompts with those of predefined text anchors. However, these techniques exhibit a considerable performance trade-off between eliminating undesirable concepts and preserving other concepts. In this paper, we systematically analyze the impact of diverse text anchors on unlearning performance. Guided by this analysis, we propose AdvAnchor, a novel approach that generates adversarial anchors to alleviate the trade-off issue. These adversarial anchors are crafted to closely resemble the embeddings of undesirable concepts to maintain overall model performance, while selectively excluding defining attributes of these concepts for effective erasure. Extensive experiments demonstrate that AdvAnchor outperforms state-of-the-art methods. Our code is publicly available at https://anonymous.4open.science/r/AdvAnchor.