Image Source Identifier with Video Deepfake and Forgery Detection using Conventional and Recurrent Neural Networks

Abstract—This paper presents a model which addresses two important forensics analysis i.e. source camera identification and deepfake/forgery detection for videos. Camera identification of images is implemented using CNN based Photo Response Non-Uniformity noise patterns and for Deepfake/Forgery we have used RNN based model that analyzes temporal and spatial inconsistencies. The source identification model is trained on multiple mobile cameras whereas the deepfake model captures the frame-wise variations and deepfake artifacts, The experimental results of the model validate the robustness of the system with an overall approximate of 93% accuracy providing a comprehensive framework for Digital Media Forensics. Keywords—Convolutional and Recurrent Neural Networks, Deepfake, Forgery, Digital Media Forensics