The proliferation of IoT devices has created risks of their abuse for unauthorized sensing/monitoring of our daily activities. Especially, the leakage of images taken by wireless spy cameras in sensitive spaces, such as hotel rooms, Airbnb rentals, public restrooms, and shower rooms, has become a serious privacy concern/threat. To mitigate/address this pressing concern, we propose a Spy Camera Finder (SCamF) that uses ubiquitous smartphones to detect and locate wireless spy cameras by analyzing encrypted Wi-Fi network traffic. Not only by characterizing the network traffic patterns of wireless cameras but also by reconstructing encoded video frame sizes from encrypted traffic, SCamF effectively determines the existence of wireless cameras on the Wi-Fi networks, and accurately verifies whether the thus-detected cameras are indeed recording users’ activities. SCamF also accurately locates spy cameras by analyzing reconstructed video frame sizes. We have implemented SCamF on Android smartphones and evaluated its performance on a real testbed across 20 types of wireless cameras. Our experimental results show SCamF to: (1) classify wireless cameras with an accuracy of 0.98; (2) detect spy cameras among the classified wireless cameras with a true positive rate (TPR) of 0.97; (3) incur low false positive rates (FPRs) of 0 and 0.031 for non-camera devices and cameras not recording the users’ activities, respectively; (4) locate spy cameras with centimeter-level distance errors.

https://dl.acm.org/doi/abs/10.1145/3564625.3564632