Title: Less Is More: Training on Low-Fidelity Images Improves Robustness to Adversarial Attacks. Abstract: Since adversarial attacks are defined relative to human perception, it may be fruitful to investigate why human perception (and biological perception in general) is robust to the types of perturbations that DNNs are convincingly deceived by. In the context of vision, we hypothesize that a factor contributing to the robustness of human visual perception is our constant exposure to low-fidelity visual stimuli. To investigate the impact, vis-à-vis adversarial robustness, of exposure to low-fidelity visual stimuli, we train and evaluate object recognition DNNs on images which have been blurred and have had their color saturation reduced. We find that DNNs trained on such images can achieve high classification accuracy over a small number of classes, while becoming significantly more robust to low-magnitude adversarial attacks. Furthermore, we design a blurring module that simulates that loss of visual acuity with increasing eccentricity by selecting the intensity of Gaussian blur at each pixel based on its distance from a given fixation point. Our results indicate that using this retina-inspired blurring mechanism, instead of blurring the entire image with the same Gaussian kernel, yields better robustness while keeping the accuracy on clean data unchanged.