CNN spatiotemporal features and fusion for surveillance video forgery detection

Abstract Surveillance cameras are widely used to provide protection and security; also their videos are used as strong evidences in the courts. Through the availability of video editing tools, it has become easy to distort these evidences. Sometimes, to hide the traces of forgery, some post-processing operations are performed after editing. Hence, the authenticity and integrity of surveillance videos have become urgent to scientifically validate. In this paper, we propose inter-frame forgeries (frame deletion, frame insertion, and frame duplication) detection system using 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatically feature extraction; Gaussian RBF multi-class support vector machine (RBF-MSVM) is used for classification process. The experimental results show that the efficiency of the proposed system for detecting all inter-frame forgeries, even when the forged videos have undergone additional post-processing operations such as Gaussian noise, Gaussian blurring, brightness modifications and compression.