  Deep neural networks have demonstrated cutting edge performance on various
tasks including classification. However, it is well known that adversarially
designed imperceptible perturbation of the input can mislead advanced
classifiers. In this paper, Permutation Phase Defense (PPD), is proposed as a
novel method to resist adversarial attacks. PPD combines random permutation of
the image with phase component of its Fourier transform. The basic idea behind
this approach is to turn adversarial defense problems analogously into
symmetric cryptography, which relies solely on safekeeping of the keys for
security. In PPD, safe keeping of the selected permutation ensures
effectiveness against adversarial attacks. Testing PPD on MNIST and CIFAR-10
datasets yielded state-of-the-art robustness against the most powerful
adversarial attacks currently available.
