提出新方法实现无需训练的强鲁棒概念擦除，同时保护模型原有功能。
To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion
- 动态发现目标与保留概念，基于扩散模型响应自适应调整编辑空间。
- 在多种攻击下保持高擦除成功率，且图像生成质量下降少（FID提升）。
- 适合需安全可控编辑的场景，如去除不良内容或版权风格。
概念擦除技术（CETs）可编辑文生图扩散模型以移除不良内容或版权风格，同时保留模型对正常概念的生成能力。现有方法在擦除强度与功能保留之间存在权衡：更强的编辑会损害非目标概念的性能。根源在于现有方法依赖静态概念库（人工设定、LLM生成或通过CLIP相似度选取），未能建模提示词如何引导去噪过程，导致模型易被触发重新引入目标概念并压制邻近良性概念。本文提出无需训练的帕斯（PARSE）框架，通过无分类器引导查询扩散模型，动态发现目标诱导概念及附近需保留的概念，并在交叉注意力值空间中进行保全感知投影，仅移除目标方向而保留保留方向。对于超出词汇索引空间的触发词，PARSE通过文本反演迭代搜索重现触发项，并仅当新触发方向不冲突于保留语义时才自适应扩展擦除子空间。此外引入平衡擦除效用评分（BEUS），通过有界单调变换与调和平均聚合，综合评估鲁棒性（多攻击下的攻击成功率）与实用性（FID）。大规模实验在NSFW内容、艺术风格和物体擦除任务上验证，相较于多个基线方法，PARSE能稳健擦除多个概念而不牺牲编辑后模型的生成质量。
原文摘要 · Abstract (English)
Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benign concepts. Current CETs face a trade-off between erasure robustness and utility: stronger edits erase the target more reliably but degrade utility on non-target concepts, and vice versa. This stems from how existing methods define what to erase and what to preserve. Many CETs rely on static concept banks specified manually, generated by LLMs, or selected by CLIP image-text similarity. Such banks do not model how prompts steer the model during denoising, leaving it vulnerable to triggers that reintroduce the target while suppressing nearby benign concepts. We present Preservation-aware Adaptive Ranked Subspace Expansion (PARSE), a training-free framework for robust concept erasure in latent diffusion models. Given a target, PARSE queries the diffusion model with classifier-free guidance to dynamically discover target-inducing erase concepts and nearby retain concepts in the model vocabulary. It then edits the cross-attention value space with a preservation-aware projection that removes target directions while leaving retain directions intact. For triggers beyond this vocabulary-indexed space, PARSE iteratively searches for re-emergence triggers by textual inversion and adaptively expands the erased subspace only when a new trigger direction does not conflict with retain semantics. We also introduce the Balanced Erasure Utility Score (BEUS), which combines robustness (ASR under multiple attacks) and utility preservation (FID) via bounded monotone transforms and harmonic mean aggregation. Experiments on NSFW, artistic style, and object erasure, with a large-scale robustness-utility analysis over many CET baselines, show that PARSE erases multiple concepts robustly without sacrificing post-edit utility.
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