598 citations · 2.9k across the 89 of their papers we have counts for
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Square Attack: a query-efficient black-box adversarial attack via random search
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion +1
We propose the Square Attack, a score-based black-box - and -adversarial attack that does not rely on local gradient information and thus is not affected by gradient…
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
Pedro Mercado, Francesco Tudisco, Matthias Hein
We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each indi…
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz, Matthias Hein, Bernt Schiele
Adversarial training yields robust models against a specific threat model, e.g., adversarial examples. Typically robustness does not generalize to previously unseen thre…
Sparse and Imperceivable Adversarial Attacks
Francesco Croce, Matthias Hein
Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On t…
Towards neural networks that provably know when they don't know
Alexander Meinke, Matthias Hein
It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don't know.…
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
Francesco Croce, Matthias Hein
The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, eve…