BUZz: BUffer Zones for defending adversarial examples in image classification

Phuong Ha Nguyen*, Kaleel Mahmood*, Lam M. Nguyen, Thanh Nguyen, Marten van Dijk

Keywords: adversarial machine learning, machine learning security

TL;DR: Achieving strong adversarial defense (coined as BUZz) comparable to existing ones based on new security concept -- buffer zones

Abstract: We propose a novel defense against all existing gradient based adversarial attacks on deep neural networks for image classification problems. Our defense is based on a combination of deep neural networks and simple image transformations. While straight forward in implementation, this defense yields a unique security property which we term buffer zones. In this paper, we formalize the concept of buffer zones. We argue that our defense based on buffer zones is secure against state-of-the-art black box attacks. We are able to achieve this security even when the adversary has access to the entire original training data set and unlimited query access to the defense. We verify our security claims through experimentation using FashionMNIST, CIFAR-10 and CIFAR-100. We demonstrate <10% attack success rate -- significantly lower than what other well-known defenses offer -- at only a price of a 15-20% drop in clean accuracy. By using a new intuitive metric we explain why this trade-off offers a significant improvement over prior work.