  Deep neural networks are vulnerable against adversarial examples. In this
paper, we propose to train and test the networks with randomly subsampled
images with high drop rates. We show that this approach significantly improves
robustness against adversarial examples in all cases of bounded L0, L2 and
L_inf perturbations, while reducing the standard accuracy by a small value. We
argue that subsampling pixels can be thought to provide a set of robust
features for the input image and, thus, improves robustness without performing
adversarial training.
