Given that existing face liveness detection algorithms perform well in a single data set but have poor generalization ability in cross multiple data sets; therefore, this study proposes a liveness detection method centering on real faces. During the data input stage, each round of training will input the real faces of multiple source domains into the network, while only randomly input false faces of one source domain. During the feature learning stage, Resnet18 serves as the backbone network to weight fuse the output features of different residual blocks based on the attention mechanism. Triple loss and adversarial loss are used to aggregate the fused real face features within each domain and cross domains, while triplet loss is used to aggregate the fused fake face features within each domain. During the classification stage, cross-entropy loss is used to classify real and false faces in all source domains. The proposed method was tested on four live face detection data sets, and the experimental results reveal that the proposed method has a lower recognition error rate and higher robustness than other methods.