8 citations · 9 across the 2 of their papers we have counts for
5 papers · 1 filter
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks
Dequan Wang, An Ju, Evan Shelhamer +2
Adversarial attacks optimize against models to defeat defenses. Existing defenses are static, and stay the same once trained, even while attacks change. We argue that models should…
Adversarial Examples for -Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams
Chawin Sitawarin, Evgenios M. Kornaropoulos, Dawn Song +1
Adversarial examples are a widely studied phenomenon in machine learning models. While most of the attention has been focused on neural networks, other practical models also suffer…
Minority Reports Defense: Defending Against Adversarial Patches
Michael McCoyd, Won Park, Steven Chen +5
Deep learning image classification is vulnerable to adversarial attack, even if the attacker changes just a small patch of the image. We propose a defense against patch attacks bas…
Minimum-Norm Adversarial Examples on KNN and KNN-Based Models
Chawin Sitawarin, David Wagner
We study the robustness against adversarial examples of kNN classifiers and classifiers that combine kNN with neural networks. The main difficulty lies in the fact that finding an…
Defending Against Adversarial Examples with K-Nearest Neighbor
Chawin Sitawarin, David Wagner
Robustness is an increasingly important property of machine learning models as they become more and more prevalent. We propose a defense against adversarial examples based on a k-n…