A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

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 copy-moves 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

Detection of video frame insertion based on constraint of human visual perception

Recently, due to availability of inexpensive and easily-operable multimedia tools, digital multimedia technology has experienced drastic advancements. At the same time, video forgery becomes much easier and makes more difficult to validate the video content. Consequently, the origin and integrity of video can no longer be taken for granted. A methodology is developed that is capable of detecting the video frame insertion based on the constraint of human visual perception. The main idea is based on the so-called differential sensitivity. That is, that the variation of brightness of neighboring video frames has some constraint. First, the video sequence is partitioned into short and overlapping sub-sequences. Second, the ratio of the temporal variation of brightness calculated at the beginning and the ending frames of each sub-sequence is computed and compared with a threshold to determine the approximate location of the video frame insertion. Third, a procedure is conducted to determine the exact location of the insertion. The success of simulation works on more than 200 video sequences. The precision rate of detection is about 94.09%, and the precision rate of detecting location of frame insertion is 84.88% on testing databas