AI RESEARCH
MultiPriv: Benchmarking Individual-Level Privacy Reasoning in Vision-Language Models
arXiv CS.CV
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ArXi:2511.16940v3 Announce Type: replace Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individuals through hierarchical chain-of-thought reasoning. However, existing privacy benchmarks remain structurally insufficient for this threat, as they primarily evaluate privacy perception while failing to address the critical risk of privacy reasoning: a VLM's ability to infer and link distributed information to construct individual profiles.