Abstract
Inter-frame forgery is the most common type of video forgery methods. However, few algorithms have been suggested for detecting this type of forgery, and the former detection methods cannot ensure the detection speed and accuracy at the same time. In this paper, we put forward a novel video forgery detection algorithm for detecting an inter-frame forgery based on Zernike opponent chromaticity moments and a coarseness feature analysis by matching from the coarse-to-fine models. Coarse detection applied to extract abnormal points is carried out first; each frame is converted from a 3D RGB color space into a 2D opposite chromaticity space combined with the Zernike moment correlation. The juggled points are then obtained exactly from abnormal points using a Tamura coarse feature analysis for fine detection. Coarse detection not only has a high-efficiency detection speed, but also a low omission ratio; however, it is accompanied by mistaken identifications, and the precision is not ideal. Therefore, fine detection was proposed to help to make up the difference in precision. The experimental results prove that this algorithm has a higher efficiency and accuracy than previous algorithms.
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