Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks

Kurt Pasque, Christopher Teska, Ruriko Yoshida, Keiji Miura, Jefferson Huang

Abstract: 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.