  In recent years, there has been growing concern over the vulnerability of
convolutional neural networks (CNNs) to image perturbations. However, achieving
general robustness against different types of perturbations remains
challenging, in which enhancing robustness to some perturbations (e.g.,
adversarial perturbations) may degrade others (e.g., common corruptions). In
this paper, we demonstrate that adversarial training with an emphasis on phase
components significantly improves model performance on clean, adversarial, and
common corruption accuracies. We propose a frequency-based data augmentation
method, Adversarial Amplitude Swap, that swaps the amplitude spectrum between
clean and adversarial images to generate two novel training images: adversarial
amplitude and adversarial phase images. These images act as substitutes for
adversarial images and can be implemented in various adversarial training
setups. Through extensive experiments, we demonstrate that our method enables
the CNNs to gain general robustness against different types of perturbations
and results in a uniform performance against all types of common corruptions.
