Adversarial Examples: Attacks and Defenses for Deep Learning

TLDR

Deep learning has achieved wide success, yet deep neural networks are vulnerable to imperceptible adversarial perturbations that can easily fool them, posing a major risk in safety‑critical applications. This paper reviews recent work on adversarial examples, summarizes generation methods, and proposes a taxonomy of these methods. The taxonomy is used to examine applications, elaborate countermeasures, and discuss three major challenges and potential solutions.

Abstract

With rapid progress and significant successes in a wide spectrum of applications, deep learning is being applied in many safety-critical environments. However, deep neural networks (DNNs) have been recently found vulnerable to well-designed input samples called adversarial examples. Adversarial perturbations are imperceptible to human but can easily fool DNNs in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention. In this paper, we review recent findings on adversarial examples for DNNs, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods. Under the taxonomy, applications for adversarial examples are investigated. We further elaborate on countermeasures for adversarial examples. In addition, three major challenges in adversarial examples and the potential solutions are discussed.