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A new algorithm is proposed for the detection and accurate localization of copy-move video forgeries. Major ingredients of the proposed algorithm are i) noise-resilient rotation-invariant features, ii) dense-field matching over the whole video by means of a suitably modified fast algorithm for approximate nearest neighbor search, iii) ad hoc fast post-processing for forgery detection and false alarm removal. Experiments carried out on a dataset publicly available for this task show promising results, suggesting also a number of directions for future research.

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We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copymoves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions.