Comparing deep neural networks against humans: object recognition when the signal gets weaker. - Robert Geirhos; David H. J. Janssen; Heiko H. Schütt; Jonas Rauber; Matthias Bethge; Felix A. Wichmann (CoRR, 2017)

Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models. - Jonas Rauber; Wieland Brendel; Matthias Bethge (CoRR, 2017)

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. - Wieland Brendel; Jonas Rauber; Matthias Bethge (ICLR (Poster), 2018)

Generalisation in humans and deep neural networks. - Robert Geirhos; Carlos R. Medina Temme; Jonas Rauber; Heiko H. Schütt; Matthias Bethge; Felix A. Wichmann (NeurIPS, 2018)

Robust Perception through Analysis by Synthesis. - Lukas Schott; Jonas Rauber; Wieland Brendel; Matthias Bethge (CoRR, 2018)

Adversarial Vision Challenge. - Wieland Brendel; Jonas Rauber; Alexey Kurakin; Nicolas Papernot; Behar Veliqi; Marcel Salathé; Sharada P. Mohanty; Matthias Bethge (CoRR, 2018)

Towards the first adversarially robust neural network model on MNIST. - Lukas Schott; Jonas Rauber; Matthias Bethge; Wieland Brendel (ICLR (Poster), 2019)

Accurate, reliable and fast robustness evaluation. - Wieland Brendel; Jonas Rauber; Matthias Kümmerer; Ivan Ustyuzhaninov; Matthias Bethge (NeurIPS, 2019)

On Evaluating Adversarial Robustness. - Nicholas Carlini; Anish Athalye; Nicolas Papernot; Wieland Brendel; Jonas Rauber; Dimitris Tsipras; Ian J. Goodfellow; Aleksander Madry; Alexey Kurakin (CoRR, 2019)

Modeling patterns of smartphone usage and their relationship to cognitive health. - Jonas Rauber; Emily B. Fox; Leon A. Gatys (CoRR, 2019)

Fast Differentiable Clipping-Aware Normalization and Rescaling. - Jonas Rauber; Matthias Bethge (CoRR, 2020)

EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy. - Jonas Rauber; Matthias Bethge; Wieland Brendel (CoRR, 2020)

Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks. - Francesco Croce; Jonas Rauber; Matthias Hein 001 (Int. J. Comput. Vis., 2020)

Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. - Jonas Rauber; Roland Zimmermann 002; Matthias Bethge; Wieland Brendel (J. Open Source Softw., 2020)

Advances in Reliably Evaluating and Improving Adversarial Robustness. - Jonas Rauber (2021)