A Digital Forensic Technique for Inter-Frame Video Forgery Detection Based on 3D CNN

With the present-day rapid growth in use of low-cost yet efficient video manipulating software, it has become extremely crucial to authenticate and check the integrity of digital videos, before they are used in sensitive contexts. For example, a CCTV footage acting as the primary source of evidence towards a crime scene. In this paper, we deal with a specific class of video forgery detection, viz., inter-frame forgery detection. We propose a deep learning based digital forensic technique using 3D Convolutional Neural Network (3D-CNN) for detection of the above form of video forgery. In the proposed model, we introduce a difference layer in the CNN, which mainly targets to extract the temporal information from the videos. This in turn, helps in efficient inter-frame video forgery detection, given the fact that, temporal information constitute the most suitable form of features for inter-frame anomaly detection. Our experimental results prove that the performance efficiency of the proposed deep learning 3D CNN model is 97% on an average, and is applicable to a wide range of video quality.