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.