  Model robustness is vital for the reliable deployment of machine learning
models in real-world applications. Recent studies have shown that data
augmentation can result in model over-relying on features in the low-frequency
domain, sacrificing performance against low-frequency corruptions, highlighting
a connection between frequency and robustness. Here, we take one step further
to more directly study the frequency bias of a model through the lens of its
Jacobians and its implication to model robustness. To achieve this, we propose
Jacobian frequency regularization for models' Jacobians to have a larger ratio
of low-frequency components. Through experiments on four image datasets, we
show that biasing classifiers towards low (high)-frequency components can bring
performance gain against high (low)-frequency corruption and adversarial
perturbation, albeit with a tradeoff in performance for low (high)-frequency
corruption. Our approach elucidates a more direct connection between the
frequency bias and robustness of deep learning models.
