Algorithm for Deep Learning in Digital Video Forensic

The rapid advancement of digital media has led to an increase in video-based misinformation, tampering, and forgery, posing serious challenges in legal, investigative, and journalistic domains. This project proposes a Deep Learning-Based Algorithm for Digital Video Forensics, designed to detect and analyze digital video manipulations efficiently. The algorithm leverages advanced Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract spatiotemporal features, detect frame-level anomalies, and identify inconsistencies indicative of video forgery. The system is implemented using Python, TensorFlow, and OpenCV, with a web-based interface built using Flask and React.js to provide an interactive and user-friendly forensic analysis platform. The results are presented with a confidence score, highlighting suspected tampered regions within the video for detailed analysis This project aims to provide a powerful forensic tool for law enforcement agencies, media organizations, and cybersecurity professionals, enabling them to verify video authenticity efficiently.