Affiliation:
1. Wuhan University, Wuhan, Hubei, China
2. Nanyang Technological University, Singapore, Singapore
3. University at Buffalo, Buffalo, United States
Abstract
Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Reference64 articles.
1. Jeffrey P. Bigham. 2019. A Camera is Watching You in Your AirBnB: And you consented to it. https://jeffreybigham.com/blog/2019/who-is-watching-you-in-your-airbnb.html.
2. Minyi Chai. 2021. CCTV reveals the black industrial chain of hotel sneak shots: there are hidden cameras in rooms and bathroom hooks.https://www.thepaper.cn/newsDetail_forward_12482186.
3. AoA-Aware Probabilistic Indoor Location Fingerprinting Using Channel State Information
4. DeWiCam
5. On Detecting Hidden Wireless Cameras: A Traffic Pattern-based Approach