Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization
Bao Wang, Alex T. Lin, Wei Zhu, Penghang Yin, Andrea L. Bertozzi, Stanley J. Osher
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/BaoWangMath/DNN-DataDependentActivationOfficialIn paperpytorch★ 0
Abstract
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 46\% to 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.