The video forensics capabilities are constantly improving in terms of evidence accumulating, analysis, processing, and storage. Video forensic analysis involves scientific investigation, comparison, and/or assessment of video files that are considered as proof in the court. In this paper, we focus on inter-frame video forgery detection and localization with respect to frame inserting and deleting that are essential from digital video forensics perspective. We design a 3-dimensional convolutional neural network (3DCNN) model for detecting video inter-frame forgery and localize forgery using multi-scale structural similarity index measurement algorithm. We introduce an absolute difference algorithm to differentiate video frames from each other which minimize the temporal redundancy and identify forgery artefacts within the video frames. This improves the 3DCNN efficiency and accuracy in detecting frame insertion and deletion forgery. The proposed model outperforms existing models in terms of accuracy, precision, recall, and F1 score in various post-processing operations, compression rates, and video length.

视频取证能力在取证、分析、处理、存储等方面不断提升。视频取证分析涉及对在法庭上被视为证据的视频文件进行科学调查、比较和/或评估。在本文中，我们重点关注帧间视频伪造检测和定位，以及从数字视频取证角度来看必不可少的帧插入和删除。我们设计了一个 3 维卷积神经网络 (3DCNN) 模型，用于使用多尺度结构相似性指数测量算法检测视频帧间伪造和定位伪造。我们引入了一种绝对差分算法来区分视频帧，从而最大限度地减少时间冗余并识别视频帧内的伪造伪影。这提高了 3DCNN 在检测帧插入和删除伪造方面的效率和准确性。所提出的模型在各种后处理操作、压缩率和视频长度的准确性、精确度、召回率和 F1 分数方面优于现有模型。