In this article we are going to understand "PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition". Fine Grained Patch-Type recognition problem: From each of cropped faces, they extract two equi-sized patches. Thus, no matter the differences in the image sizes, there is no need of image distortion. This helps in training the network as well, as in every epoch some new patches from the same face would be cropped acting as an additional augmentation along with random horizontal flip and random rotation. Asymmetric margin based softmax loss: Instead of using Cross-entropy, this loss allowed the authors to specify margin of separation between classification boundaries, thus reducing the chances of wrong classification.