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Detection of fake 3D video using CNN

机译:使用CNN检测假3D视频

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摘要

In this paper, a novel automatic fake and the real 3D video recognition scheme is proposed to distinguish the 3D video converted from the 2D video using 2D to 3D conversion process (say fake 3D) from the 3D video captured using direct capturing of the 3D camera (say real 3D). To identify the real and fake 3D, pre-filtration is done using the dual tree complex wavelet transform to emerge the edge and vertical and horizontal parallax characteristics of real and fake 3D videos. Convolution neural network (CNN) is used to train the 3D characteristics to distinguish the fake 3D videos from the real ones. A comprehensive set of experiments has been carried out to justify the efficacy of the proposed scheme over the existing literature.
机译:在本文中,提出了一种新颖的自动伪造和真实的3D视频识别方案,以区分使用2D到3D转换过程(例如伪造3D)从2D视频转换而来的3D视频与使用3D摄像机直接捕获而捕获的3D视频。 (例如真实的3D)。为了识别真实和伪造的3D,使用对偶树复数小波变换进行预过滤,以显示真实和伪造的3D视频的边缘,垂直和水平视差特征。卷积神经网络(CNN)用于训练3D特性,以区分假3D视频和真实3D视频。已经进行了一套全面的实验,以证明所提出的方案相对于现有文献的有效性。

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