2022/04/01 by Julia Grabinski, Grabinski, Julia, Steffen Jung +5 · 6 citations
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Integrated Circuits and Semiconductor Failure Analysis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.00491
openalex publication_date 2022/04/01 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.