(UbiComp'23) CamRadar: Hidden Camera Detection Leveraging Amplitude-modulated Sensor Images Embedded in Electromagnetic Emanations


Hidden cameras in sensitive locations have become an increasing threat to personal privacy all over the world. Because the camera is small and camouflaged, it is difficult to detect the presence of the camera with naked eyes. Existing works on this subject have either only covered using wireless transmission to detect cameras, or using other methods which are cumbersome in practical use. In this paper, we introduce a new direction that leverages the unintentional electromagnetic (EM) emanations of the camera to detect it. We first find that the digital output of the camera’s image sensor will be amplitude-modulated to the EM emanations of the camera’s clock. Thus, changes in the scope of the camera will directly cause changes in the camera’s EM emanations, which constitutes a unique characteristic for a hidden camera. Based on this, we propose a novel camera detection system named CamRadar, which can filter out potential camera EM emanations from numerous EM signals quickly and achieve accurate hidden camera detection. Benefitting from the camera’s EM emanations, CamRadar will not be limited by the camera transmission types or the detection angle. Our extensive real-world experiments using CamRadar and 19 hidden cameras show that CamRadar achieves a fast detection (in 16.75s) with a detection rate of 93.23% as well as a low false positive rate of 3.95%.

In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies/UbiComp 2023