In view of the significant texture difference between fake faces and real faces， this paper proposes a face forgery detection model based on texture features. Firstly， ResNet18 is used as the backbone network， and combined with the channel attention mechanism and residual network to solve the problem of network degradation， in order to establish the connection between channels to extract deep features. Secondly， the autocorrelation matrix is used to quantify the correlation between image blocks， and the features of different scales in the image are captured to obtain global statistical features. Finally， the Gabor filter is introduced after each pooling layer of the autocorrelation module to extract the local texture features of the image， providing a comprehensive description of the image content， and the Softmax function is used to perform hierarchical classification. Experimental results show that this method effectively improves the detection accuracy for fake images edited by different image enhancement methods.