Tropical Decision Boundaries for Neural Networks Are Robust Against
  Adversarial Attacks
Kurt Pasque Christopher Teska Ruriko Yoshida Keiji Miura Jefferson Huang
We introduce a simple, easy to implement, and computationally efficient
tropical convolutional neural network architecture that is robust against
adversarial attacks. We exploit the tropical nature of piece-wise linear neural
networks by embedding the data in the tropical projective torus in a single
hidden layer which can be added to any model. We study the geometry of its
decision boundary theoretically and show its robustness against adversarial
attacks on image datasets using computational experiments.