4 papers
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…
On the Robustness of Deep K-Nearest Neighbors
Chawin Sitawarin, David Wagner
Despite a large amount of attention on adversarial examples, very few works have demonstrated an effective defense against this threat. We examine Deep k-Nearest Neighbor (DkNN), a…