Real-Time Detection of Inter-Frame Video Forgeries in Surveillance Videos

Detection of video forgery is a critical requirement to ensure integrity of video data and with an increase in the use of cameras and dependability of surveillance systems, it becomes more critical. In this paper, video forgery detection is proposed based on the concept of exponential weighted moving average of optical flow variation factors. This mechanism relies on the fact that optical flow variation factor is almost continuous in an original video. However, discontinuity points in the optical flow variation factor are introduced in forged video which are not visible with the naked eye. The proposed mechanism detects inter-frame forgery process, i.e., frame deletion, insertion, and duplication in surveillance videos. The proposed detection mechanism is deployed on cloud server where a live video stream is received from different ATM vestibules. Upon detection, an alert message is sent to the concerned bank for immediate action. Experiments are performed on the videos captured from ATM vestibules of a bank. The accuracy and latency of proposed mechanism manifest pragmatism of the proposed idea.