Privacy-Preserving Selective Video Surveillance

Abstract

The pervasive and intrusive surveillance practices have raised a widespread concern amongst zillions of people about the invasion of their privacy. The privacy breaches in the existing mass-surveillance system are mainly attributed to the exploits of vulnerabilities by adversaries and abuse of cameras by people in charge of them. As a result, there has been a tremendously pressing demand from the public to make the surveillance system privacy-conscious. In this paper, we propose PriSev, a privacy-preserving selective video surveillance method, which enables selective-surveillance where only video frames containing aggressive and suspicious behavioral patterns, like gun brandishing or/and fist-raising, are made available for view by security personnel in the surveillance operation center and for storage. By introducing a lightweight dynamic chaotic image enciphering (DyCIE) scheme, the proposed PriSev method enables onsite object detection and frame encryption at the network edge where the video is created. At the fog/cloud layer, frame decryption is efficiently performed followed by deep-neural-network (DNN) based frame-filtering and selective storage that runs on a surveillance server. In addition, a multiagent system is introduced for the exchange of deciphering keys between the sending and receiving agents. Extensive experimental study and performance analyses corroborate that the proposed PriSev method is able to efficiently perform a privacy-preserving selective surveillance in real-time.