Dynamic Background Video Forgery Detection using Gaussian Mixture Model

Video as evidence holds an important place in a court case and therefore the integrity of video must be proven. Various studies had been done in video forensics and most of them is only focused on a certain type of forgery, such as histogram correlation analysis that only focused on detecting temporally forged videos with static background. Improving histogram correlation analysis with foreground detection using Gaussian mixture model makes it possible to detect spatially forged videos and further improves its accuracy in detecting dynamic background video. Applying proposed method will yield 20.83% improved detection's accuracy, 34.42% increased localization's precision, and 39.17% increased localization's re-call. Furthermore, object's definition introduced by foreground detection will also open up new possibility to detect spatially forged video.