Detection of Deep Video Frame Interpolation via Learning Dual-Stream Fusion CNN in the Compression Domain

Deep learning-based Video Frame Interpolation (Deep VFI) diminishes the visual traces of the conventional one such that it is challenging for the current VFI detectors. Therefore, it is necessary to identify the presence of deep interpolated frames (DIF) in a video. This paper proposed a hybrid neural network to localize the DIF by learning spatio-temporal representations from the residual and motion vector information in the compression domain. Firstly, the residual and motion vector of motion regions are maintained by an intra-prediction constraints. Then, inherent tampering traces are further highlighted through subtracting the estimate of the residual or motion vector by virtue of residual modulation or MV refinement network. Finally, an attention-based dual-stream network is designed to jointly learn discriminative representations from the enhancement traces. Deep VFI video datasets created by the state-of-the-art deep VFI methods, have been evaluated, and extensive experimental results clearly demonstrate that our approach can achieve state-of-the-art performance compared with conventional methods.