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arXiv · 2609.33481

What Does It Mean to Forget a Person? Individual-Level Unlearning in Vision-Language Models

Xiongtao Sun·Hui Li·Tiantong Wu·Jiaming Zhang·Fuyao Zhang·Wen Jun Tan
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Abstract

Erasing individual identities from Vision-Language Models (VLMs) is uniquely challenging because personal data is entangled across modalities rather than stored as isolated attributes. However, existing multimodal unlearning benchmarks primarily evaluate attribute-centric forgetting, overlooking the more critical objective of individual-level unlearning: eliminating a model's ability to access, link, and reconstruct target-related information across modalities. To address this gap, we propose IDUnlearn-Bench, the first benchmark for individual-level multimodal unlearning in VLMs. It represents each individual as connected multimodal evidence and evaluates four task families: attribute access, identity access, identity binding, and identity reconstruction. Experiments on representative VLMs and unlearning methods show that successful attribute-centric forgetting often leaves substantial identity-level knowledge intact and can be non-monotonic: reducing one form of risk may amplify another. Models may suppress selected information while still identifying the target, linking records, or reconstructing the individual. These findings reveal a fundamental gap between forgetting information about a person and forgetting the person as a whole.

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Xiongtao Sun, Hui Li, Tiantong Wu, Jiaming Zhang, Fuyao Zhang, Wen Jun Tan. 2026-09-27. What Does It Mean to Forget a Person? Individual-Level Unlearning in Vision-Language Models. https://arxiv.org/abs/2609.33481

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