  Recent improvements in deep learning models and their practical applications
have raised concerns about the robustness of these models against adversarial
examples. Adversarial training (AT) has been shown effective to reach a robust
model against the attack that is used during training. However, it usually
fails against other attacks, i.e. the model overfits to the training attack
scheme. In this paper, we propose a simple modification to the AT that
mitigates the mentioned issue. More specifically, we minimize the perturbation
$\ell_p$ norm while maximizing the classification loss in the Lagrangian form.
We argue that crafting adversarial examples based on this scheme results in
enhanced attack generalization in the learned model. We compare our final model
robust accuracy against attacks that were not used during training to closely
related state-of-the-art AT methods. This comparison demonstrates that our
average robust accuracy against unseen attacks is 5.9% higher in the CIFAR-10
dataset and is 3.2% higher in the ImageNet-100 dataset than corresponding
state-of-the-art methods. We also demonstrate that our attack is faster than
other attack schemes that are designed for unseen attack generalization, and
conclude that it is feasible for large-scale datasets.
