579 citations · 2.2k across the 13 of their papers we have counts for
3 papers · 1 filter
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Wieland Brendel, Jonas Rauber, Matthias Bethge
Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs. So far it was unclear how much risk adversarial perturbations carry for the s…
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, Matthias Bethge
Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversar…
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Robert Geirhos, David H. J. Janssen, Heiko H. Schütt +3
Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual…