Objective With the rapid development of video tampering technology the gap between original videos and tampered videos is narrowing. Existing detection methods need to improve accuracy and generalization performance in detecting video tampering. Therefore a video tampering detection method based on spatiotemporal consistency was proposed. Methods Firstly the video was processed with different sampling strides. Temporal convolutional kernels were used to extract temporal features in high-sampling-rate data while spatial convolutional kernels focused on extracting spatial features in low-sampling-rate data. A lateral connection was established between high-sampling-rate and low- sampling-rate video data to obtain a better representation of spatiotemporal features. Additionally a Transformer model was used to extract inconsistencies in the spatiotemporal feature sequence to detect tampered videos. Results The improved method was tested on the high-quality and low-quality datasets of FaceForensics + + achieving AUC values of 99. 47% and 93. 05% respectively. Furthermore in-domain cross-forgery experiments on the FaceForensics + + dataset and cross-dataset experiments on the Celeb-DF dataset showed competitive performance compared with current mainstream detection algorithms. Ablation experiments validated the effectiveness of each module. Conclusion Based on the experimental results of various groups the proposed method demonstrates superior detection accuracy in domain-specific performance testing compared with existing algorithms. Additionally it exhibits better generalization performance in cross- domain testing verifying that the combined spatiotemporal convolution and Transformer model can enhance model generalization performance.