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Videos can be manipulated by duplicating a sequence of consecutive frameswith the goal of concealing or imitating a specific content in the same video.In this paper, we propose a novel coarse-to-fine framework based on deepConvolutional Neural Networks to automatically detect and localize such frameduplication. First, an I3D network finds coarse-level matches between candidateduplicated frame sequences and the corresponding selected original framesequences. Then a Siamese network based on ResNet architecture identifiesfine-level correspondences between an individual duplicated frame and thecorresponding selected frame. We also propose a robust statistical approach tocompute a video-level score indicating the likelihood of manipulation orforgery. Additionally, for providing manipulation localization information wedevelop an inconsistency detector based on the I3D network to distinguish theduplicated frames from the selected original frames. Quantified evaluation ontwo challenging video forgery datasets clearly demonstrates that this approachperforms significantly better than four recent state-of-the-art methods.

A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Forged Videos