Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks

Fuente: arXiv
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Main Authors: Pasque, Kurt, Teska, Christopher, Yoshida, Ruriko, Miura, Keiji, Huang, Jefferson
Format: Preprint
Published: 2024
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author Pasque, Kurt
Teska, Christopher
Yoshida, Ruriko
Miura, Keiji
Huang, Jefferson
author_facet Pasque, Kurt
Teska, Christopher
Yoshida, Ruriko
Miura, Keiji
Huang, Jefferson
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2402_00576
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks
Pasque, Kurt
Teska, Christopher
Yoshida, Ruriko
Miura, Keiji
Huang, Jefferson
Machine Learning
Cryptography and Security
Computer Vision and Pattern Recognition
Combinatorics
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.
title Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks
topic Machine Learning
Cryptography and Security
Computer Vision and Pattern Recognition
Combinatorics
url https://arxiv.org/abs/2402.00576