PRNU-based source camera identification on smart video surveillance: a practical approach

Abstract

Video surveillance is very common nowadays, with systems deployed in conventional networks, as well as in the cloud and IoT domains. While the internet-based video surveillance systems provide ease of operation, at the same time they are prone to cyber attacks. Therefore, video authentication cannot be guaranteed if someone hacks into the system and gets to the video source. In order to identify the video source, a source identification method employing the PRNU (Photo-Response Non-Uniformity) noise as the detecting signal has been devised. PRNU is a kind of sensor pattern noise, which can be found from every digital image captured by a digital camera. It has been proved to be useful in image and video camera source identification. However, the challenges of real-life applications have not been fully addressed, especially on the IoT-based video surveillance. In this paper, we present a practical approach of the PRNU-based source verification scheme incorporated into the smart video surveillance system with limited resources, such as low computation power at the edge of the networks. The performance of the proposed scheme is evaluated through simulation tests on different cameras taking video scenes at different periods in a day. It comes up with the results of an efficient and effective prototype for our method, which can be comparable to the state-of-the-art techniques in related works.