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Small, low-cost, wireless cameras are becoming increasingly commonplacemaking surreptitious observation of people more difficult to detect. Previouswork in detecting hidden cameras has only addressed limited environments insmall spaces where the user has significant control of the environment. Toaddress this problem in a less constrained scope of environments, we introducethe concept of similarity of simultaneous observation where the user utilizes acamera (Wi-Fi camera, camera on a mobile phone or laptop) to compare timingpatterns of data transmitted by potentially hidden cameras and the timingpatterns that are expected from the scene that the known camera is recording.  To analyze the patterns, we applied several similarity measures anddemonstrated an accuracy of over 87% and and F1 score of 0.88 using anefficient threshold-based classification. We used our data set to train aneural network and saw improved results with accuracy as high as 97% and an F1score over 0.95 for both indoors and outdoors settings. We further extend thiswork against an attacker who is capable of delaying when the video is sent.With the new approach, we see increased F1 scores above 0.98 for the originaldata and delayed data. From these results, we conclude that similarity ofsimultaneous observation is a feasible method for detecting hidden wirelesscameras that are streaming video of a user. Our work removes significantlimitations that have been put on previous detection methods.

Detecting Hidden Webcams with Delay-Tolerant Similarity of Simultaneous Observation