176 citations · 217 across the 48 of their papers we have counts for
5 papers · 1 filter
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
Peter Lorenz, Dominik Strassel, Margret Keuper +1
Recently, RobustBench (Croce et al. 2020) has become a widely recognized benchmark for the adversarial robustness of image classification networks. In its most commonly reported su…
Detecting AutoAttack Perturbations in the Frequency Domain
Peter Lorenz, Paula Harder, Dominik Strassel +2
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very…
Is Differentiable Architecture Search truly a One-Shot Method?
Jonas Geiping, Jovita Lukasik, Margret Keuper +1
Differentiable architecture search (DAS) is a widely researched tool for the discovery of novel architectures, due to its promising results for image classification. The main benef…
Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space
Kalun Ho, Franz-Josef Pfreundt, Janis Keuper +1
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on sta…
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain
Paula Harder, Franz-Josef Pfreundt, Margret Keuper +1
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, c…