The location of the smallest object in a scene plays an essential role in the perception of a viewer. Any tampering with it, may evolve in adverse consequences especially with surveillance videos of banks, ATMs, traffic monitoring etc. Therefore, a scientific approach is required to thoroughly observe the fine details of tampering (forgery) in a video. A spatio-temporal detection method is proposed using convolutional neural network (CNN) to detect as well as localise the forged region in a forged video frame. The proposed method is employed in two stages. The first stage is detecting forged frames using proposed temporal CNN, while the second stage is localising the forged region in a novel way using proposed spatial CNN. The vital element of a video, i.e. motion residual is used to train the proposed network. Thus, making the network comprehensive in detecting the object-based forgery in HD videos. The performance of the proposed method is evaluated on SYSU-OBJFORG dataset (object-based video forgery dataset) and a derived test dataset of variable length and frame size videos. The results are compared with state-of-the-art methods to prove the efficacy of the proposed method.

中文翻译：

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基于CNN的高清视频基于对象的伪造中的伪造区域定位

场景中最小物体的位置在观看者的感知中起着至关重要的作用。任何篡改行为都可能带来不利后果，尤其是在银行，ATM机，流量监控等监控视频中。因此，需要一种科学的方法来彻底观察视频中篡改（伪造）的细节。提出了一种使用卷积神经网络（CNN）的时空检测方法来检测并定位伪造视频帧中的伪造区域。该方法分为两个阶段。第一阶段是使用提议的时间CNN检测伪造的帧，而第二阶段是使用提议的空间CNN以新颖的方式定位伪造区域。视频的重要元素，即运动残差，用于训练提议的网络。从而，使网络能够全面检测高清视频中基于对象的伪造。在SYSU-OBJFORG数据集（基于对象的视频伪造数据集）和可变长度和帧大小视频的派生测试数据集上评估了该方法的性能。将结果与最新方法进行比较，以证明该方法的有效性。