Efficient detection of intra/inter-frame video copy-move forgery: A hierarchical coarse-to-fine method

With a simple forgery technique but a realistic result, video copy-move forgery has currently become one of the most popular tampering manners. In the last couple of years, various new techniques deriving from machine intelligence and pattern recognition have been widely proposed for image forensics. However, it still faces a very challenging task in the field of video copy-move forgery for four reasons: i) Low F₁ score and high false-alarm; ii) Lack of a synthesis processing framework; iii) Weak detection robustness and accuracy; iv) Low efficiency. A novel Hierarchical Coarse-to-Fine framework for effective video copy-move forgery detection is proposed to overcome these challenges: i) In the coarse forgery frame-pair matching, the coarse copy-move frame-pairs matching algorithm with the newly proposed two-pass filters can locate real forgery frame-pairs (FFP) and also reduce false-alarm. ii) Through further analysis of the actual FFP, the detection of intra-frame and inter-frame copy-move forgeries can be accurately and simultaneously determined. iii) In the fine keypoint-pairs matching, our newly designed two-hierarchical keypoint-pair filtering can accurately localize the forgery region at pixel level under various adverse conditions. iv) The novel Hierarchical Coarse-to-Fine framework (together with the newly designed algorithms above) considers only the real FFP and true keypoint-pairs for computation, resulting in higher efficiency and accuracy. Finally, Delaunay Triangulation-based region filling is employed to indicate the forgery regions. Compared to the latest methods, our algorithm has been tested extensively and found to be the best at detecting forgeries, with a top score of F₁=0.77 and no false-alarms, even under different types of attacks, as validated by the well-known GRIP dataset.