title: Thermometer Encoding: One Hot Way To Resist Adversarial Examples
abstract: It is well known that it is possible to construct "adversarial examples"
  for neural networks: inputs which are misclassified by the network
  yet indistinguishable from true data. We propose a simple
  modification to standard neural network architectures, thermometer
  encoding, which significantly increases the robustness of the network to
  adversarial examples. We demonstrate this robustness with experiments
  on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that
  models with thermometer-encoded inputs consistently have higher accuracy
  on adversarial examples, without decreasing generalization.
  State-of-the-art accuracy under the strongest known white-box attack was 
  increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10.
  We explore the properties of these networks, providing evidence
  that thermometer encodings help neural networks to
  find more-non-linear decision boundaries.
authors: Jacob Buckman Aurko Roy Colin Raffel Ian Goodfellow
hIndex: Ian Goodfellow 64