The Limitations of Deep Learning in Adversarial Settings

@article{Papernot2016TheLO,
  title={The Limitations of Deep Learning in Adversarial Settings},
  author={Nicolas Papernot and Patrick D. McDaniel and Somesh Jha and Matt Fredrikson and Z. Berkay Celik and Ananthram Swami},
  journal={2016 IEEE European Symposium on Security and Privacy (EuroS&P)},
  year={2016},
  pages={372-387}
}
Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of… CONTINUE READING
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