提出新方法防止文本生成模型概念复活，仅需6分钟即可实现持久删除。
Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks
- 通过构建核心梯度空间，将更新投影到正交补集以阻止概念恢复
- 在风格概念上彻底消除复活，物体概念复活时间显著延迟
- 兼容现有方法，适合需要长期保护模型隐私的研究者
针对文本到图像扩散模型的机器遗忘技术旨在不进行昂贵重训练的情况下，选择性移除不良概念。当前方法普遍存在缺陷：即使下游数据完全无关，模型微调后被删除的概念仍会重新出现。本文将分类任务中的投影梯度遗忘（PGU）方法适配至扩散模型领域，作为事后加固步骤。通过从保留概念激活中构建核心梯度空间（CGS），并将梯度更新投影至其正交补空间，确保后续微调无法撤销已实现的删除效果。该方法在现有方法（ESD、UCE、Receler）基础上应用后，可彻底消除风格概念的复活现象，并显著延缓物体概念的复活，运行时间约为6分钟，远低于Meta-Unlearning所需的约2小时。研究发现PGU与Meta-Unlearning具有互补性，性能优劣取决于概念编码方式；保留概念的选择应基于视觉特征相似性而非语义分组。
原文摘要 · Abstract (English)
Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining. Current unlearning methods share a common weakness: erased concepts return when the model is fine-tuned on downstream data, even when that data is entirely unrelated. We adapt Projected Gradient Unlearning (PGU) from classification to the diffusion domain as a post-hoc hardening step. By constructing a Core Gradient Space (CGS) from the retain concept activations and projecting gradient updates into its orthogonal complement, PGU ensures that subsequent fine-tuning cannot undo the achieved erasure. Applied on top of existing methods (ESD, UCE, Receler), the approach eliminates revival for style concepts and substantially delays it for object concepts, running in roughly 6 minutes versus the ~2 hours required by Meta-Unlearning. PGU and Meta-Unlearning turn out to be complementary: which performs better depends on how the concept is encoded, and retain concept selection should follow visual feature similarity rather than semantic grouping.
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