Exposing video inter-frame forgery via histogram of oriented gradients and motion energy image

Abstract

Inter-frame forgery is a common type of video forgery to destroy the video evidence. It occurs in the temporal domain such as frame deletion, frame insertion, frame duplication, and frame shuffling. These forms of forgery are more frequently produced in a surveillance video because the camera position and the scene are relatively stable, where the tampering process is easy to operate and imperceptible. In this paper, we propose an efficient method for inter-frame forgery detection based on histogram of oriented gradients (HOG) and motion energy image (MEI). HOG is obtained from each image as a discriminative feature. In order to detect frame deletion and insertion, the correlation coefficients are used and abnormal points are detected via Grabb’s test. In addition, MEI is applied to edge images of each shot to detect frame duplication and shuffling. Experimental results prove that the proposed method can detect all inter-frame forgeries and achieve higher accuracy with lower execution time.