In recent years, with the rapid development of deepfake technology, a large number of deepfake videos have emerged on the Internet, which poses a huge threat to national politics, social stability, and personal privacy. Although many existing deepfake detection methods exhibit excellent performance for known manipulations, their detection capabilities are not strong when faced with unknown manipulations. Therefore, in order to obtain better generalization ability, this paper analyzes global and local inter-frame dynamic inconsistencies from the perspective of spatial and frequency domains, and proposes a Local region Frequency Guided Dynamic Inconsistency Network (LFGDIN). The network includes two parts: Global SpatioTemporal Network (GSTN) and Local Region Frequency Guided Module (LRFGM). The GSTN is responsible for capturing the dynamic information of the entire face, while the LRFGM focuses on extracting the frequency dynamic information of the eyes and mouth. The LRFGM guides the GTSN to concentrate on dynamic inconsistency in some significant local regions through local region alignment, so as to improve the model’s detection performance. Experiments on the three public datasets (FF++, DFDC, and Celeb-DF) show that compared with many recent advanced methods, the proposed method achieves better detection results when detecting deepfake videos of unknown manipulation types.