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 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 .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.

小型，低成本的无线摄像机正变得越来越普遍，这使得对人的秘密观察变得更加难以发现。检测隐藏摄像机的先前工作仅解决了用户对环境有明显控制的小空间中的有限环境。为了在较少限制的环境范围内解决此问题，我们引入了同时观察的相似性概念，即用户使用摄像头（Wi-Fi摄像头，移动电话或笔记本电脑上的摄像头）比较由潜在用户传输的数据的时序模式隐藏的摄像机以及已知摄像机正在录制的场景中预期的时序模式。为了分析模式，我们应用了几种相似性度量，并证明了超过87％的准确性和F1得分为0。88使用有效的基于阈值的分类。我们使用我们的数据集训练了一个神经网络，并在室内和室外设置中看到了改善的结果，准确性高达97％，F1得分超过0.95。我们将这项工作进一步扩展到能够延迟视频发送时间的攻击者。使用新方法，我们看到原始数据和延迟数据的F1分数增加了0.98以上。根据这些结果，我们得出结论，同时观察的相似性是一种可行的方法，用于检测正在隐藏用户视频的无线摄像机。我们的工作消除了以前的检测方法所受到的重大限制。我们将这项工作进一步扩展到能够延迟视频发送时间的攻击者。使用新方法，我们看到原始数据和延迟数据的F1分数增加了0.98以上。根据这些结果，我们得出结论，同时观察的相似性是一种可行的方法，用于检测正在隐藏用户视频的无线摄像机。我们的工作消除了以前的检测方法所存在的重大限制。我们将这项工作进一步扩展到能够延迟视频发送时间的攻击者。使用新方法，我们看到原始数据和延迟数据的F1分数增加了0.98以上。根据这些结果，我们得出结论，同时观察的相似性是一种用于检测正在隐藏用户视频的无线摄像机的可行方法。我们的工作消除了以前的检测方法所存在的重大限制。