579 citations · 845 across the 33 of their papers we have counts for
4 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…
Comment on "Biologically inspired protection of deep networks from adversarial attacks"
Wieland Brendel, Matthias Bethge
A recent paper suggests that Deep Neural Networks can be protected from gradient-based adversarial perturbations by driving the network activations into a highly saturated regime.…
Learning to represent signals spike by spike
Wieland Brendel, Ralph Bourdoukan, Pietro Vertechi +2
A key question in neuroscience is at which level functional meaning emerges from biophysical phenomena. In most vertebrate systems, precise functions are assigned at the level of n…