  Over the last years, Convolutional Neural Networks (CNNs) have been the
dominating neural architecture in a wide range of computer vision tasks. From
an image and signal processing point of view, this success might be a bit
surprising as the inherent spatial pyramid design of most CNNs is apparently
violating basic signal processing laws, i.e. Sampling Theorem in their
down-sampling operations. However, since poor sampling appeared not to affect
model accuracy, this issue has been broadly neglected until model robustness
started to receive more attention. Recent work [17] in the context of
adversarial attacks and distribution shifts, showed after all, that there is a
strong correlation between the vulnerability of CNNs and aliasing artifacts
induced by poor down-sampling operations. This paper builds on these findings
and introduces an aliasing free down-sampling operation which can easily be
plugged into any CNN architecture: FrequencyLowCut pooling. Our experiments
show, that in combination with simple and fast FGSM adversarial training, our
hyper-parameter free operator significantly improves model robustness and
avoids catastrophic overfitting.
