How to effectively identify all kinds of attacked faces is an urgent problem to be solved in the process of face recognition. The face anti-spoofing methods based on deep learning have high performance， but also bring a large number of parameters and calculation， so they cannot be deployed in mobile or embedded devices. To solve the above problems， a face anti-spoofing method based on regional blocking and lightweight network was proposed. Firstly， the training samples were randomly blocked. Then， a lightweight network based on attention mechanism was designed for feature extraction and image classification. Finally， in order to improve the detection accuracy， data augmentation was conducted on the test samples based on regional blocking. Experimental results show that the proposed model reaches 100% accuracy on REPLAY-ATTACK and CASIA-FASD datasets. At the same time， the proposed model obtains 99.49% accuracy and 0.458 0% Average Classification Error Rate （ACER） on the Depth modal of CASIA-SURF dataset， which are much better than those obtained by convolutional neural networks such as ResNet and ShuffleNet. And the parameter amount of the model is only 0.258 2 MB. In practical applications， the end-to-end lightweight network structure makes the proposed model easier to be deployed on mobile devices for real-time face anti-spoofing detection.