  Deep learning models often face challenges when handling real-world image
corruptions. In response, researchers have developed image corruption datasets
to evaluate the performance of deep neural networks in handling such
corruptions. However, these datasets have a significant limitation: they do not
account for all corruptions encountered in real-life scenarios. To address this
gap, we present MUFIA (Multiplicative Filter Attack), an algorithm designed to
identify the specific types of corruptions that can cause models to fail. Our
algorithm identifies the combination of image frequency components that render
a model susceptible to misclassification while preserving the semantic
similarity to the original image. We find that even state-of-the-art models
trained to be robust against known common corruptions struggle against the low
visibility-based corruptions crafted by MUFIA. This highlights the need for
more comprehensive approaches to enhance model robustness against a wider range
of real-world image corruptions.
