Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models W Brendel*, J Rauber*, M Bethge International Conference on Learning Representations 2018, 2018 | 1599 | 2018 |
On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 1014 | 2019 |
Generalisation in humans and deep neural networks R Geirhos*, CRM Temme*, J Rauber*, HH Schuett, M Bethge, ... Advances in Neural Information Processing Systems 31 (2018), 2018 | 729 | 2018 |
Foolbox: A Python toolbox to benchmark the robustness of machine learning models J Rauber*, W Brendel*, M Bethge Reliable Machine Learning in the Wild Workshop, ICML 2017, 2017 | 722 | 2017 |
Towards the first adversarially robust neural network model on MNIST L Schott*, J Rauber*, W Brendel, M Bethge International Conference on Learning Representations 2019, 2018 | 445 | 2018 |
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library N Papernot, F Faghri, N Carlini, I Goodfellow, R Feinman, A Kurakin, ... arXiv preprint arXiv:1610.00768, 2018 | 430 | 2018 |
Comparing deep neural networks against humans: object recognition when the signal gets weaker R Geirhos, DHJ Janssen, HH Schütt, J Rauber, M Bethge, FA Wichmann arXiv preprint arXiv:1706.06969, 2017 | 312 | 2017 |
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX J Rauber, R Zimmermann, M Bethge, W Brendel Journal of Open Source Software 5 (53), 2607, 2020 | 219 | 2020 |
Accurate, reliable and fast robustness evaluation W Brendel, J Rauber, M Kümmerer, I Ustyuzhaninov, M Bethge Advances in Neural Information Processing Systems, 12861-12871, 2019 | 135 | 2019 |
Adversarial Vision Challenge (Results) W Brendel, J Rauber, A Kurakin, N Papernot, B Veliqi, SP Mohanty, ... The NeurIPS'18 Competition, 129-153, 2020 | 66* | 2020 |
Adversarial Vision Challenge W Brendel, J Rauber, A Kurakin, N Papernot, B Veliqi, M Salathé, ... Neural Information Processing Systems 2018, 2018 | 66 | 2018 |
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks F Croce*, J Rauber*, M Hein arXiv preprint arXiv:1903.11359, 2019 | 37 | 2019 |
Fast Differentiable Clipping-Aware Normalization and Rescaling J Rauber, M Bethge arXiv preprint arXiv:2007.07677, 2020 | 17 | 2020 |
Modeling patterns of smartphone usage and their relationship to cognitive health J Rauber, E Fox, L Gatys Machine Learning for Health Workshop, NeurIPS 2019, 2019 | 8 | 2019 |
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy J Rauber, M Bethge, W Brendel arXiv preprint arXiv:2008.04175, 2020 | 6 | 2020 |
Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs R Geirhos, P Rubisch, J Rauber, CRM Temme, C Michaelis, W Brendel, ... 19th Annual Meeting of the Vision Sciences Society (VSS 2019), 209c-209c, 2019 | 3 | 2019 |
Device-usage processing for generating inferred user states LA Gatys, E Fox, J Rauber US Patent App. 16/848,421, 2021 | | 2021 |
Foolbox Documentation J Rauber, W Brendel Read the Docs, 2018 | | 2018 |