Publications

Towards the first adversarially robust neural network model on MNIST

arXiv preprint, 2018

Lukas Schott*, Jonas Rauber*, Matthias Bethge, Wieland Brendel

@article{schott2018towards,
  title={Towards the first adversarially robust neural network model on MNIST},
  author={Schott, Lukas and Rauber, Jonas and Brendel, Wieland and Bethge, Matthias},
  journal={arXiv preprint arXiv:1805.09190},
  year={2018}
}

arXiv

Generalisation in humans and deep neural networks

Advances in Neural Information Processing Systems 31, 2018

Robert Geirhos*, Carlos R. Medina Temme*, Jonas Rauber*, Heiko H. Schuett, Matthias Bethge, Felix A. Wichmann

@incollection{geirhos2018generalisation,
  title = {Generalisation in humans and deep neural networks},
  author = {Geirhos, Robert and Temme, Carlos R Medina and Rauber, Jonas and Schuett, Heiko H and Bethge, Matthias and Wichmann, Felix A},
  booktitle = {Advances in Neural Information Processing Systems 31},
  year = {2018},
}

arXiv Code

Adversarial Vision Challenge

Competition Track of the 32nd Conference on Neural Information Processing Systems, 2018

Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge

@article{brendel2018adversarial,
  title={Adversarial Vision Challenge},
  author={Brendel, Wieland and Rauber, Jonas and Kurakin, Alexey and Papernot, Nicolas and Veliqi, Behar and Salath{\'e}, Marcel and Mohanty, Sharada P and Bethge, Matthias},
  journal={arXiv preprint arXiv:1808.01976},
  year={2018}
}

arXiv Competition Track crowdAI

Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

International Conference on Learning Representations, 2018

Wieland Brendel*, Jonas Rauber*, Matthias Bethge

@inproceedings{brendel2018decisionbased,
  title={Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models},
  author={Wieland Brendel and Jonas Rauber and Matthias Bethge},
  booktitle={International Conference on Learning Representations},
  year={2018},
  url={https://openreview.net/forum?id=SyZI0GWCZ},
}

arXiv OpenReview Code

Foolbox: A Python toolbox to benchmark the robustness of machine learning models

Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning, 2017

Jonas Rauber, Wieland Brendel, Matthias Bethge

@article{rauber2017foolbox,
  title={Foolbox: A Python toolbox to benchmark the robustness of machine learning models},
  author={Rauber, Jonas and Brendel, Wieland and Bethge, Matthias},
  journal={arXiv preprint arXiv:1707.04131},
  year={2017},
  url={http://arxiv.org/abs/1707.04131},
  archivePrefix={arXiv},
  eprint={1707.04131},
}

arXiv Workshop Code