  Deep neural networks (DNNs) have been shown to be vulnerable to adversarial
attacks -- subtle, perceptually indistinguishable perturbations of inputs that
change the response of the model. In the context of vision, we hypothesize that
an important contributor to the robustness of human visual perception is
constant exposure to low-fidelity visual stimuli in our peripheral vision. To
investigate this hypothesis, we develop \RBlur, an image transform that
simulates the loss in fidelity of peripheral vision by blurring the image and
reducing its color saturation based on the distance from a given fixation
point. We show that compared to DNNs trained on the original images, DNNs
trained on images transformed by \RBlur are substantially more robust to
adversarial attacks, as well as other, non-adversarial, corruptions, achieving
up to 25\% higher accuracy on perturbed data.
