研究机器人视觉的隐私风险，发现深度图和语义图更安全，低分辨率图像可有效保护隐私。
Privacy Risks of Robot Vision: A User Study on Image Modalities and Resolution
- 通过用户调研对比不同图像模态与分辨率对隐私感知的影响。
- 32×32分辨率的RGB图被认为几乎足够保护隐私，16×16则被视为完全安全。
- 深度图和语义分割图被广泛认为隐私风险较低，适合敏感场景使用。
用户隐私是移动服务机器人在个人或敏感环境中应用时的关键关切。然而，许多机器人下游任务需要摄像头，可能引发隐私风险。为更好地理解用户对视觉数据隐私的感知，我们开展了一项用户研究，探讨不同图像模态和分辨率如何影响用户的隐私担忧。结果表明，深度图像普遍被认为隐私安全，同样高比例受访者也认为语义分割图像具有类似安全性。此外，多数参与者认为32×32分辨率的RGB图像几乎足以保障隐私，而大多数人认为16×16分辨率可完全实现隐私保护。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。