Can Vision Models Truly Forget? Mirage: Representation-Level Certification of Visual Unlearning
The paper introduces Mirage, a framework for auditing visual unlearning in machine learning models. It highlights the limitations of current methods that only certify forgetting at the output level. The authors present findings that emphasize the need for representation-aware evaluation standards in federated unlearning research.
- ▪Mirage comprises four diagnostics: Linear Probe Recovery, Centered Kernel Alignment, Feature Separability Scoring, and Layer-Wise Recovery Analysis.
- ▪The study reveals that methods passing output-level certification still retain significant class structure in their representations.
- ▪No existing method achieves high utility, output-level forgetting, and representation-level forgetting simultaneously.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20282 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20282 (cs) [Submitted on 19 May 2026] Title:Can Vision Models Truly Forget? Mirage: Representation-Level Certification of Visual Unlearning Authors:Zhenyu Yu, Yangchen Zeng, Chunlei Meng, Guangzhen Yao, Shuigeng Zhou View a PDF of the paper titled Can Vision Models Truly Forget? Mirage: Representation-Level Certification of Visual Unlearning, by Zhenyu Yu and 4 other authors View PDF HTML (experimental) Abstract:Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-level metrics.
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