Corpus ID: 195776342

Accurate, reliable and fast robustness evaluation

@article{Brendel2019AccurateRA,
  title={Accurate, reliable and fast robustness evaluation},
  author={W. Brendel and Jonas Rauber and Matthias K{\"u}mmerer and Ivan Ustyuzhaninov and M. Bethge},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.01003}
}

W. Brendel, Jonas Rauber, +2 authors M. Bethge

Published 2019

Computer Science, Mathematics

ArXiv

Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. Despite much attention, however, progress towards more robust models is significantly impaired by the difficulty of evaluating the robustness of neural network models. Today's methods are either fast but brittle (gradient-based attacks), or they are fairly reliable but slow (score- and decision-based attacks). We here… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed