Abstract: Aiming at the problems that the current deepfake detection method lacks attention to the spatial domain information of the image and the high complexity of the model, a channel adaptive depth forgery detection method using spatial domain enhancement is proposed. Firstly, spatial domain features of the images are extracted and normalized, which will be passed into the network as the fourth channel; secondly, SE-Layer module is incorporated into the backbone network to reconstruct the weights of the four channels, effectively addressing the heterogeneity issue between channels; finally, a semi-automatic pre-training strategy is designed to further improve the training efficiency and accuracy of the model. Taking deepfake face detection as an example, the detection experiment is conducted on seven datasets from different sources. The results show that the method achieves higher accuracy compared with the baseline method although no data augumentation technique is used.