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In order to solve the problems of inaccurate temporal detection and location of the object removal tampered video, a video tamper forensics method based on 3D dual-stream network was proposed. Firstly, the spatial rich model (SRM) layer was used to extract the high-frequency information from video frames. Secondly, the improved 3D convolution (C3D) network was used as the feature extractor of the dual-stream network to extract the high-frequency information and low-frequency information from the high-frequency frame and the original video frame respectively. Finally, through compact bilinear pooling (CBP) layer, two sets of different feature vectors were fused into one set of feature vectors for classification prediction. The experimental results demonstrate that the classification accuracy of the proposed method in all video frames has an advantage in SYSU-OBJFORG dataset, which makes the temporal detection and location of object removal tampered video more accurate. [ABSTRACT FROM AUTHOR]