Privacy Risks of Robot Vision: A User Study on Image Modalities and Resolution

2025/05/12 by Xuying Huang, Huang, Xuying, Sicong Pan +3 · 1 citation
Psychology · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Privacy, Security, and Data Protection #Robotics (cs.RO) #Social Robot Interaction and HRI

paper · pdf · doi:10.48550/arxiv.2505.07766

openalex publication_date 2025/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

User privacy is a crucial concern in robotic applications, especially when mobile service robots are deployed in personal or sensitive environments. However, many robotic downstream tasks require the use of cameras, which may raise privacy risks. To better understand user perceptions of privacy in relation to visual data, we conducted a user study investigating how different image modalities and image resolutions affect users' privacy concerns. The results show that depth images are broadly viewed as privacy-safe, and a similarly high proportion of respondents feel the same about semantic segmentation images. Additionally, the majority of participants consider 32*32 resolution RGB images to be almost sufficiently privacy-preserving, while most believe that 16*16 resolution can fully guarantee privacy protection.

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