  Small, low-cost, wireless cameras are becoming increasingly commonplace
making surreptitious observation of people more difficult to detect. Previous
work in detecting hidden cameras has only addressed limited environments in
small spaces where the user has significant control of the environment. To
address this problem in a less constrained scope of environments, we introduce
the concept of similarity of simultaneous observation where the user utilizes a
camera (Wi-Fi camera, camera on a mobile phone or laptop) to compare timing
patterns of data transmitted by potentially hidden cameras and the timing
patterns that are expected from the scene that the known camera is recording.
  To analyze the patterns, we applied several similarity measures and
demonstrated an accuracy of over 87% and and F1 score of 0.88 using an
efficient threshold-based classification. We used our data set to train a
neural network and saw improved results with accuracy as high as 97% and an F1
score over 0.95 for both indoors and outdoors settings. We further extend this
work 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 original
data and delayed data. From these results, we conclude that similarity of
simultaneous observation is a feasible method for detecting hidden wireless
cameras that are streaming video of a user. Our work removes significant
limitations that have been put on previous detection methods.
