  Adversarial images are designed to mislead deep neural networks (DNNs),
attracting great attention in recent years. Although several defense strategies
achieved encouraging robustness against adversarial samples, most of them fail
to improve the robustness on common corruptions such as noise, blur, and
weather/digital effects (e.g. frost, pixelate). To address this problem, we
propose a simple yet effective method, named Progressive Data Augmentation
(PDA), which enables general robustness of DNNs by progressively injecting
diverse adversarial noises during training. In other words, DNNs trained with
PDA are able to obtain more robustness against both adversarial attacks as well
as common corruptions than the recent state-of-the-art methods. We also find
that PDA is more efficient than prior arts and able to prevent accuracy drop on
clean samples without being attacked. Furthermore, we theoretically show that
PDA can control the perturbation bound and guarantee better generalization
ability than existing work. Extensive experiments on many benchmarks such as
CIFAR-10, SVHN, and ImageNet demonstrate that PDA significantly outperforms its
counterparts in various experimental setups.
