Video Forgery Detection Using Machine Learning

– With the rapid advancement of digital media editing tools, video forgeries have become increasingly sophisticated and harder to detect, posing serious threats to information security, legal evidence integrity, and digital content authenticity. This project presents a machine learning-based approach for the detection of video forgeries, focusing on identifying tampered frames, splicing, and copy-move operations. The system extracts spatiotemporal features and motion inconsistencies using techniques such as optical flow analysis, frame differencing, and noise residual examination. These features are then analyzed using supervised machine learning models, including Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), to distinguish.