Face-spoofing detection plays an important role in ensuring the security of face recognition systems. Most multi-modal methods based on deep learning improve their accuracy by utilizing information from RGB, depth, and infrared. In fact, given the cost and application conditions, it is difficult to obtain all these data. Therefore, it is especially important to exploit single-modal images to extract more detailed information. To address the above problems, we propose an efficient two-stream convolutional network, which takes an original image and its wavelet-transformed image as input. Then, we design two branches to extract the features, with the wavelet branch more conducive to mining the detailed information. Finally, we adopt three loss functions to supervise the two branches and the fused branch respectively, and each branch can be scored separately. The extensive experiments demonstrate that our model can achieve satisfactory performance on the datasets, with replay-attack and CASIA-FASD achieving 100% accuracy.