  Aliasing is a highly important concept in signal processing, as careful
consideration of resolution changes is essential in ensuring transmission and
processing quality of audio, image, and video. Despite this, up until recently
aliasing has received very little consideration in Deep Learning, with all
common architectures carelessly sub-sampling without considering aliasing
effects. In this work, we investigate the hypothesis that the existence of
adversarial perturbations is due in part to aliasing in neural networks. Our
ultimate goal is to increase robustness against adversarial attacks using
explainable, non-trained, structural changes only, derived from aliasing first
principles. Our contributions are the following. First, we establish a
sufficient condition for no aliasing for general image transformations. Next,
we study sources of aliasing in common neural network layers, and derive simple
modifications from first principles to eliminate or reduce it. Lastly, our
experimental results show a solid link between anti-aliasing and adversarial
attacks. Simply reducing aliasing already results in more robust classifiers,
and combining anti-aliasing with robust training out-performs solo robust
training on $L_2$ attacks with none or minimal losses in performance on
$L_{\infty}$ attacks.
