Performance enhancement of PRNU-based source identification for smart video surveillance

This paper introduces a current signal-based source verification (SSV) system for images in video surveillance networks including the cloud. Using a signal, the well-known photo-response non-uniformity (PRNU) can be used, which is unique and intrinsic in every digital image taken by a source camera, like fingerprints. The SSV system using PRNU has proved before to be useful for reliable video source identification in both network- and cloud-based video surveillance. However, in the era of smart living, security video systems have become part of the IoT devices which typically have limited resources such as low computation, power, storage and memory. To address these problems in the IoT applications, the effects of I-frames only and infra-red night scenes are studied as well as two proposed approaches for the SSV system. Then a hybrid version of the SSV scheme is further suggested, in combination with the best approach using averaged noise residues (for reduced false positive rate), and a recent technique using spatial domain averaged frames (for reduced computational complexity). The enhanced performance of the improved SSV system for smart video surveillance has been verified through tests.