Dense moment feature index and best match algorithms for video copy-move forgery detection

Abstract

This paper proposes a video copy-move forgery detection method to effectively address inter/intra-frame forgeries both at the frame and pixel level. First, a unified moment framework is proposed to extract multi-dimensional dense moment features from the video effectively. Second, a novel feature representation method takes each feature sub-map index to represent its every dimensional feature and then concatenates to a 9-digit dense moment feature index. Third, an inter-frame best match algorithm is proposed to search the 9-digit dense moment feature index of each pixel to find its best matches. All the best matches construct the best match map. Fourth, an inter-frame post-processing algorithm identifies the inter-frame forgery video in the best match map firstly and then indicates the corresponding inter-frame forgery regions. Otherwise, the intra-frame post-processing algorithm re-searches the best match of every pixel in each independent frame and then indicates the intra-frame forgery regions. If the video does not belong to the intra-frame forgeries, the video is determined as a genuine one. The experimental results show that the proposed method is effective at addressing the forensics of the genuine/forgery video and locating the inter/intra-frame copy-move forgeries both at the frame and pixel level.