Copy-Move Forgery Detection in Videos Using Machine Learning Algorithm

Digital multimedia applications have greatly increased in recent years as a result of improvements in network technology, low-cost multimedia devices, intelligent image or video editing software, and widespread acceptance of digital multimedia coding standards. One of the most difficult aspects of video forensics is establishing whether a video is authenticated or not. When videos are used as fundamental evidence to affect judgments, such as in a court of law, this can be a critical duty. This paper proposes using the statistical feature of noise remnant to detect passive forgeries in a digital video. To find the revamped section of a video by analyzing the temporal correlation of block-level noise remnants. Significant simulation findings are given to show that the approach can accurately identify the altered region and predict alteration parameters with high reliability.