Frame duplication detection forgery in videos

Malicious users can edit videos for hiding an object or incident. These people can make forgery as frame deletion from videos, insertion frames into videos and duplication some of frames in the videos. The changes made on the video leave statistical traces in the video. Forgery detection is done from these traces. In this thesis scope, frame duplication forgery detection in videos is studied. The current methods in literature is investigated, new methods that robust against compression attacks is suggested, and experimental results are reported. The proposed methods perform effective forgery detection and provide faster execution time compared to other methods in the literature.