Sriram Sami, Sean Rui Xiang Tan, Bangjie Sun, and Jun Han. 2021. LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys ‘21). Association for Computing Machinery, New York, NY, USA, 288–301. Acceptance Rate: 17.9% (25 of 139). Most downloaded paper across all years of ACM SenSys. [paper] [repo]

Abstract

Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate.