On Detecting Adversarial Perturbations

@article{Metzen2017OnDA,
  title={On Detecting Adversarial Perturbations},
  author={Jan Hendrik Metzen and Tim Genewein and Volker Fischer and Bastian Bischoff},
  journal={CoRR},
  year={2017},
  volume={abs/1702.04267}
}
Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small “detector” subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial… CONTINUE READING
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