SecureCam: Selective Detection and Encryption Enabled Application for Dynamic Camera Surveillance Videos

Abstract

Using dynamic surveillance cameras for security has significantly increased the privacy concerns for captured individuals. Malicious users may misuse these videos by performing Replay and/or Man-in-the-Middle attacks during storage or recording over the network. Considering these risks, this paper proposes an effective security application SecureCam based on selective detection (focused moving objects) and protection using encryption. For object detection, this paper implements a novel low computational unsupervised learning algorithm i.e. Motion-Fusion (MF) for more precise motion detection in the mobile camera videos. After that, selective encryption (SE) is applied by the lightweight Chacha20 cipher to the detected video parts. Proposed SecureCam is extensively evaluated based on performance analysis, security analysis and computational complexity. For object detection, the comparative evaluation shows that the MF algorithm outperforms traditional state-of-the-art dense optical flow (DOF) algorithm with an average (mean) difference increase: in the accuracy of 54%; and in the precision of 42% making it computationally effective for such videos. The visual results with 21% encryption space ratio (ESR) indicate that the videos are sufficiently protected against identification. Overall comparative evaluation with existing approaches also affirm the significance and utility of proposed SecureCam for internet of multimedia things (IoMT) environment.