  We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by
using an interpolating function as the output activation. This data-dependent
activation remarkably improves both the generalization and robustness of DNN.
In the CIFAR10 benchmark, we raise the robust accuracy of the adversarially
trained ResNet20 from $\sim 46\%$ to $\sim 69\%$ under the state-of-the-art
Iterative Fast Gradient Sign Method (IFGSM) based adversarial attack. When we
combine this data-dependent activation with total variation minimization on
adversarial images and training data augmentation, we achieve an improvement in
robust accuracy by 38.9$\%$ for ResNet56 under the strongest IFGSM attack.
Furthermore, We provide an intuitive explanation of our defense by analyzing
the geometry of the feature space.
