Objective Current Deepfake detection methods primarily focus on global forgery features leading to poor generalization performance of the model due to insufficient utilization of local texture contrast features. To address this issue a Deepfake detection model based on local texture difference feature enhancement was proposed aiming to improve detection accuracy and generalization by exploring intrinsic spatial forgery patterns in forged images. Methods Firstly the model captured both pixel intensity and pixel gradient by center difference convolution operation to obtain more accurate local texture difference information and improve the sensitivity to forged images. Secondly a dual-layer attention module was constructed aiming to use spatial attention to learn location-sensitive weighting information and adaptively adjust the channel importance through channel attention to locate the position of important texture disparity features and enhance the representation of texture disparity features. Results Experiments on high-quality and low-quality FaceForensics + + datasets obtained average accuracies of 97. 36% and 92. 37% respectively while cross-dataset experiments on the Celeb- DF dataset obtained better generalization performance than current state-of-the-art detection models. Extensive ablation studies validate the effectiveness of the proposed method. Conclusion Experiments show that integrating center difference convolution and a dual-layer attention module enables the model to better capture texture difference information in images adapt to different scenarios and compression rates in forgery detection and effectively improve the accuracy and generalization of Deepfake detection.