  Deep convolutional neural networks (DCNNs) have revolutionized computer
vision and are often advocated as good models of the human visual system.
However, there are currently many shortcomings of DCNNs, which preclude them as
a model of human vision. There are continuous attempts to use features of the
human visual system to improve the robustness of neural networks to data
perturbations. We provide a detailed analysis of such bio-inspired models and
their properties. To this end, we benchmark the robustness of several
bio-inspired models against their most comparable baseline DCNN models. We find
that bio-inspired models tend to be adversarially robust without requiring any
special data augmentation. Additionally, we find that bio-inspired models beat
adversarially trained models in the presence of more real-world common
corruptions. Interestingly, we also find that bio-inspired models tend to use
both low and mid-frequency information, in contrast to other DCNN models. We
find that this mix of frequency information makes them robust to both
adversarial perturbations and common corruptions.
