No Trespassing: Ground-View Adversarial Patches for Privacy-Aware Management in COTS Robot Vacuum Cleaner

Robot vacuum cleaners (RVCs) with autonomous navigation and decision-making capabilities have become an integral part of modern homes. During their operations, these devices may inadvertently enter privacy-sensitive areas, leading to potential privacy breaches. However, existing defense methods risk exposing the location of private areas, require root privileges, or are designed for infrared sensors that are ineffective for camera-based RVCs. To overcome these limitations, we propose a novel solution, a ground-view adversarial patch named GPatch, preventing RVCs from entering privacy-sensitive areas. Users only need to place GPatch at the entrance of restricted areas to prevent an RVC's unauthorized access, while also providing a warning to unauthorized individuals. We evaluate GPatch in realworld environments with an average success rate of 87.27%, and experimental results demonstrate its effectiveness, robustness, and transferability, making it a practical, user-friendly, and reliable solution for safeguarding privacy in home environments.

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Publication Details

Journal
IEEE Transactions on Dependable and Secure Computing
Published
2025-08-28
DOI
https://doi.org/10.1109/tdsc.2025.3603988
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
Field-Weighted Citation Impact
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article

No Trespassing: Ground-View Adversarial Patches for Privacy-Aware Management in COTS Robot Vacuum Cleaner

Hangcheng Cao, Yuguang Fang, Guowen Xu, Xinyuan Qian et al.
IEEE Transactions on Dependable and Secure Computing
Physical Unclonable Functions (PUFs) and Hardware Security
article

No Trespassing: Ground-View Adversarial Patches for Privacy-Aware Management in COTS Robot Vacuum Cleaner

Hangcheng Cao, Yuguang Fang, Guowen Xu, Xinyuan Qian, Tao Ni, Hongwei Li, Shuai Yuan, Rui Zhang, Qingchuan Zhao
article en

Abstract

Robot vacuum cleaners (RVCs) with autonomous navigation and decision-making capabilities have become an integral part of modern homes. During their operations, these devices may inadvertently enter privacy-sensitive areas, leading to potential privacy breaches. However, existing defense methods risk exposing the location of private areas, require root privileges, or are designed for infrared sensors that are ineffective for camera-based RVCs. To overcome these limitations, we propose a novel solution, a ground-view adversarial patch named GPatch, preventing RVCs from entering privacy-sensitive areas. Users only need to place GPatch at the entrance of restricted areas to prevent an RVC's unauthorized access, while also providing a warning to unauthorized individuals. We evaluate GPatch in realworld environments with an average success rate of 87.27%, and experimental results demonstrate its effectiveness, robustness, and transferability, making it a practical, user-friendly, and reliable solution for safeguarding privacy in home environments.

IEEE Transactions on Dependable and Secure ComputingVol. 23(1)
University of Electronic Science and Technology of China (CN), City University of Hong Kong (HK)
Openalex Percentile: Top 76%
Physical Unclonable Functions (PUFs) and Hardware Security
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No Trespassing: Ground-View Adversarial Patches for Privacy-Aware Management in COTS Robot Vacuum Cleaner — Hangcheng Cao, Yuguang Fang, et al. · IEEE Transactions on Dependable and Secure Computing (2025) | TGRS Research Map | TGRS