Metric Unreliability in Multimodal Machine Unlearning: A Systematic Analysis and Principled Unified Score
Machine unlearning in Vision-Language Models (VLMs) is required for compliance with the General Data Protection Regulation (GDPR), yet current evaluation practices are inconsistent. We present the first systematic study of metric reliability in multimodal unlearning. Five standard metrics, Forget Accuracy (FA), Retain Accuracy (RA), Membership Inference Attack (MIA), Activation Distance (AD), and JS divergence (JS), yield conflicting method rankings across three VQA benchmarks (MLLMU-Bench, UnLOK-VQA, MMUBench). Kendall tau analysis over 36 unlearned LLaVA-1.5-7B models reveals two opposing clusters, {FA, RA, MIA} and {AD, JS}, with tau_FA_AD = -0.26, reproduced on BLIP-2 OPT-2.7B. Agreem...
| Authors | Abdullah Ahmad Khan, Hamid Laga, Ferdous Sohel |
|---|---|
| Primary category | cs.CV |
| Published | 4 May 2026 |
| Last updated at source | 8 May 2026 |
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