LU-500：用于概念遗忘的标志基准

中文摘要 AI 辅助

概念遗忘越来越多地用于限制文本到图像模型中受保护或不安全视觉概念的再现。现有评估大多研究主导整个图像的目标，如风格、宽泛对象类别或人像身份等，公司标志相对未被充分研究。标志会产生不同的失败模式。我们引入LU-500，一个基于全球500强公司构建的标志遗忘基准，包含近10000个精心策划的文本查询和标志图像对，有显式和隐式上下文两种跟踪。为避免将任务简化为二元检测器分数，定义了多粒度协议。实验表明评估方法在不改变非目标内容的情况下难以去除标志证据。进一步分析提示空间多智能体基线ProLU，其虽能通过去除标志诱导语义改善局部擦除，但也说明提示过滤不能替代权重级解缠结。相关分析表明未来标志遗忘可能需要空间感知控制。

英文摘要

Concept unlearning is increasingly used to limit the reproduction of protected or unsafe visual concepts in text-to-image models. Existing evaluations, however, mostly study targets that dominate the whole image, such as styles, broad object categories, or portrait-like identities, leaving company logos comparatively underexamined. Logos create a different failure mode: a small localized mark can carry the entire protected concept, must be visually precise to remain recognizable, and can be triggered implicitly by products, storefronts, packaging, or advertisements even when the word ``logo'' is absent. We introduce LU-500, a logo-unlearning benchmark built from Fortune Global 500 companies to study this localized and semantically entangled setting. LU-500 contains nearly 10,000 curated text-query and logo-image pairs, with an explicit track (LUex-500) and an implicit contextual track (LUim-500). To avoid reducing the task to a binary detector score, we define a multi-grained protocol that evaluates both local logo removal and global image preservation in pixel and latent spaces. Experiments on representative inference-time methods, including NP, SLD, and SEGA, and compatible fine-tuning-based methods such as ESD and Forget-Me-Not, show that the evaluated methods struggle to remove logo evidence without changing non-target content. We further analyze ProLU, a prompt-space multi-agent baseline: it improves local erasure by removing logo-inducing semantics, but also illustrates why prompt filtering is not a substitute for weight-level disentanglement. Correlation analyses over logo area, location, and structural complexity suggest that future logo unlearning may need spatially aware controls, such as SSIM-guided constraints, rather than purely global concept suppression.

发表机构

• Shanghai Jiao Tong University（上海交通大学）

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