DeepDeSpy: A Deep Learning-Based Wireless Spy Camera Detection System

Abstract

Spy cameras planted in various private places, such as motels, hotels, homestays ( i.e., Airbnb), and restrooms, have raised immense privacy concerns. Wi-Fi spy cameras are used extensively by various adversaries because of easy installability, followed by size reduction. To prevent invasions of privacy, most studies have detected wireless cameras based on video traffic analysis and require additional synchronous data from external sensors or stimulus hardware to confirm the user’s motion. Such supplements make the users uncomfortable, requiring extra effort and time for setting. This paper proposes an effective spy camera detection system called DeepDeSpy to detect the recording of a spy camera with no effort from the user. The core idea is using the channel state information (CSI) and the network traffic from the camera to detect whether the wireless camera records the movements of the user. The CSI signal is prone to motion, and detecting motion from an enormous amount of CSI data in real-time is challenging. This was handled by leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep learning methods. Such synergistic CNN and BiLSTM deep learning models enable instant and accurate detection by automatically extracting meaningful features from the sequential raw CSI data. The feasibility of DeepDeSpy was verified by implementing it on both a PC and a smartphone and evaluating it in real-life scenarios (e.g., various room sizes and user physical activities). The average accuracy achieved in different real-life settings was approximately 96%, reaching 98.9% with intensive physical activity in the large-size room. Moreover, the ability to achieve instant detection on a smartphone within only a one-second response time makes it workable for real-time applications.