  Adversarial training has been successfully applied to build robust models at
a certain cost. While the robustness of a model increases, the standard
classification accuracy declines. This phenomenon is suggested to be an
inherent trade-off. We propose a model that employs feature prioritization by a
nonlinear attention module and $L_2$ feature regularization to improve the
adversarial robustness and the standard accuracy relative to adversarial
training. The attention module encourages the model to rely heavily on robust
features by assigning larger weights to them while suppressing non-robust
features. The regularizer encourages the model to extract similar features for
the natural and adversarial images, effectively ignoring the added
perturbation. In addition to evaluating the robustness of our model, we provide
justification for the attention module and propose a novel experimental
strategy that quantitatively demonstrates that our model is almost ideally
aligned with salient data characteristics. Additional experimental results
illustrate the power of our model relative to the state of the art methods.
