Surveillance cameras are widely used to provide protection and security; also their videos are used as strong evidences in the courts. Through the availability of video editing tools, it has become easy to distort these evidences. Sometimes, to hide the traces of forgery, some post-processing operations are performed after editing. Hence, the authenticity and integrity of surveillance videos have become urgent to scientifically validate. In this paper, we propose inter-frame forgeries (frame deletion, frame insertion, and frame duplication) detection system using 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatically feature extraction; Gaussian RBF multi-class support vector machine (RBF-MSVM) is used for classification process. The experimental results show that the efficiency of the proposed system for detecting all inter-frame forgeries, even when the forged videos have undergone additional post-processing operations such as Gaussian noise, Gaussian blurring, brightness modifications and compression.

监视摄像机被广泛用于提供保护和安全性。他们的视频也被用作法庭的有力证据。通过提供视频编辑工具，可以很容易地扭曲这些证据。有时，为了隐藏伪造的痕迹，在编辑后会执行一些后处理操作。因此，监视视频的真实性和完整性已变得迫切需要科学验证。在本文中，我们提出使用时空信息和融合的二维卷积神经网络（2D-CNN）进行帧间伪造（帧删除，帧插入和帧复制）检测系统，以自动进行深度特征提取；高斯RBF多类支持向量机（RBF-MSVM）用于分类过程。