AI RESEARCH
See No Evil: Semantic Context-Aware Privacy Risk Detection for AR
arXiv CS.CV
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ArXi:2604.22805v1 Announce Type: new Augmented reality (AR) systems pose unique privacy risks due to their continuous capture of visual data. Existing AR privacy frameworks lack semantic understanding of visual content, limiting their effectiveness in detecting context-dependent privacy risks. We propose PrivAR, which leverages vision language models (VLMs) with chain-of-thought prompting for contextual privacy risk detection in AR environments.