  DNNs trained on natural clean samples have been shown to perform poorly on
corrupted samples, such as noisy or blurry images. Various data augmentation
methods have been recently proposed to improve DNN's robustness against common
corruptions. Despite their success, they require computationally expensive
training and cannot be applied to off-the-shelf trained models. Recently, it
has been shown that updating BatchNorm (BN) statistics of an off-the-shelf
model on a single corruption improves its accuracy on that corruption
significantly. However, adopting the idea at inference time when the type of
corruption is unknown and changing decreases the effectiveness of this method.
In this paper, we harness the Fourier domain to detect the corruption type, a
challenging task in the image domain. We propose a unified framework consisting
of a corruption-detection model and BN statistics update that improves the
corruption accuracy of any off-the-shelf trained model. We benchmark our
framework on different models and datasets. Our results demonstrate about 8%
and 4% accuracy improvement on CIFAR10-C and ImageNet-C, respectively.
Furthermore, our framework can further improve the accuracy of state-of-the-art
robust models, such as AugMix and DeepAug.
