  Although Convolutional Neural Networks (CNNs) have high accuracy in image
recognition, they are vulnerable to adversarial examples and
out-of-distribution data, and the difference from human recognition has been
pointed out. In order to improve the robustness against out-of-distribution
data, we present a frequency-based data augmentation technique that replaces
the frequency components with other images of the same class. When the training
data are CIFAR10 and the out-of-distribution data are SVHN, the Area Under
Receiver Operating Characteristic (AUROC) curve of the model trained with the
proposed method increases from 89.22\% to 98.15\%, and further increased to
98.59\% when combined with another data augmentation method. Furthermore, we
experimentally demonstrate that the robust model for out-of-distribution data
uses a lot of high-frequency components of the image.
