FAKE FACE DETECTION METHOD BASED ON MULTI-SCALE TRANSFORMER
Considering the problems of low classification accuracy and poor generalization ability in most current fake face classification methods,a fake face classification method combining multi-scale transformer and convolutional block attention module is proposed.The multi-scale transformer is used to learn the high-level semantic features of the feature map extracted by the convolutional layer,and the convolutional block attention module is used to enhance this high-level semantic feature to make it more discriminative,and use the en-hanced features for classification.Experimental results show that the method can significantly improve the accuracy and generalization ability of fake face classification.
multi-scale transformerconvolutional block attention modulefake face classificationhigh-level semantic features
NETL
NSTL
万方数据
