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Deep learning models often face challenges when handling real-world imagecorruptions. In response, researchers have developed image corruption datasetsto evaluate the performance of deep neural networks in handling suchcorruptions. However, these datasets have a significant limitation: they do notaccount for all corruptions encountered in real-life scenarios. To address thisgap, we present MUFIA (Multiplicative Filter Attack), an algorithm designed toidentify the specific types of corruptions that can cause models to fail. Ouralgorithm identifies the combination of image frequency components that rendera model susceptible to misclassification while preserving the semanticsimilarity to the original image. We find that even state-of-the-art modelstrained to be robust against known common corruptions struggle against the lowvisibility-based corruptions crafted by MUFIA. This highlights the need formore comprehensive approaches to enhance model robustness against a wider rangeof real-world image corruptions.

Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions