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 deepfake image detection method using spatial domain feature 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 even without data augmentation, the method in this article is better than the baseline method, with an accuracy of 99.60% and an AP index of 98.00% on the StyleGAN2 dataset with the worst performance.