Robust Detection of Object-based Forgeries in Advanced Video using Deep Convolutional Neural Networks

Video forgery detection is a critical component of digital forensics, addressing the complexities introduced by manipulated video content. This paper presents an advanced approach for detecting object-based video forgeries using CNN to enhance the accuracy and efficiency of identifying tampered objects in video sequences. The method builds on existing CNN models, introducing innovative improvements in data preprocessing, network design, and training techniques to optimize performance. OpenCV is used for video frame extraction and preprocessing, enabling the manipulation and analysis of video data. The proposed approach demonstrates superior performance over traditional methods, providing enhanced precision and resilience in identifying object-based video forgeries.This research contributes to the advancement of video forgery detection and emphasizes the importance of CNNs in tackling the issues associated with digital video manipulation. The findings suggest potential directions for future research, including localization of forged regions and the application of CNNs for video sequences with lower bitrate or resolution.