# See No Evil: Semantic Context-Aware Privacy Risk Detection for AR

arXiv: https://arxiv.org/abs/2604.22805 · HTML: https://mlsanity.com/paper/2604.22805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality](https://mlsanity.com/paper/2501.12553/) (2025)
- [Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World](https://mlsanity.com/paper/2310.16061/) (2023)
- [ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting](https://mlsanity.com/paper/2502.14780/) (2025)
- [Facial Identity Anonymization via Intrinsic and Extrinsic Attention Distraction](https://mlsanity.com/paper/2406.17219/) (2024)
- [Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition](https://mlsanity.com/paper/2502.06100/) (2025)
- [Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users](https://mlsanity.com/paper/2508.09245/) (2025)
- [Verifiable Access Control for Augmented Reality Localization and Mapping](https://mlsanity.com/paper/2203.13308/) (2022)
- [User Understanding of Privacy Permissions in Mobile Augmented Reality: Perceptions and Misconceptions](https://mlsanity.com/paper/2506.20207/) (2025)

## Abstract (arXiv)

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. PrivAR uses visual scene cues to infer potential sensitive information types, such as identifying password notes in office environments through contextual reasoning. PrivAR detects and obfuscates textual content, preventing exposure of sensitive information while preserving contextual cues necessary for VLM inference. Additionally, we investigate contextually-informed warning interfaces to enhance user privacy awareness. Experiments on a real-world AR dataset show that PrivAR achieves superior accuracy (81.48%) and F1-score (84.62%) compared to baselines, while reducing privacy leakage rate to 17.58%. User studies evaluating contextually-informed warning interfaces provide insights into effective privacy-aware AR design.