Deepfake techniques have dramatically improved the realism of synthetic faces in recent years. And the fake videos it generates are more difficult to distinguish than traditional forgery methods. Based on the characteristic that visual artifacts of depth forgery images often exist in the high frequency components of shallow features in feature extraction network, a detection algorithm for depth forgery images oriented to the high frequency components of shallow features is designed. First, a high-frequency residual extraction module based on Laplace's pyramid with better filtering performance is designed to address high-pass filters' shortcomings. Second, the Convolutional Block Attention Module (CBAM) is used to increase the weights of key regions of the feature map and key feature channels to improve the spatial and channel correlation of the feature map in the enhancement module. Then, an image gradient loss is designed to prevent the loss of high-frequency information as the network deepens to address the problem of low learning priority of high-frequency components in deep networks. Finally, gradient-centralization is introduced into the AdamW optimizer to solve the problems of long training time and poor generalization of deep forgery detection models. Two models proposed outperform mainstream algorithms in terms of accuracy when validated on the FaceForensics++ and Celeb-DF datasets, demonstrating the algorithms' effectiveness and generalization.