Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization
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Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization

  • Author(s): Wang, Bao
  • Lin, Alex T
  • Zhu, Wei
  • Yin, Penghang
  • Bertozzi, Andrea L
  • Osher, Stanley J
  • et al.
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 $\sim 46\%$ 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.

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