Image Source Identifier with Video Deepfake and Forgery Detection using Conventional and Recurrent Neural Networks
Year: 2025
Type: article
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 NonUniformity 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.
Author: Rahul Daga
Institution: Vishwakarma Institute of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410616476