Security Matters: A Survey on Adversarial Machine Learning

TL;DR

This survey comprehensively reviews adversarial machine learning, covering its foundational concepts, attack and defense strategies, and recent research developments, highlighting the importance of understanding vulnerabilities in deep learning models.

Contribution

It provides a thorough overview of adversarial machine learning, including foundational theories, attack/defense methods, and recent advances, serving as a valuable resource for researchers.

Findings

Deep learning models are vulnerable to imperceptible perturbations.

Various attack and defense strategies have been developed.

Adversarial examples pose significant security challenges.

Abstract

Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake. It always involves a defending side, usually a classifier, and an attacking side that aims to cause incorrect output. The earliest studies on the adversarial examples for machine learning algorithms start from the information security area, which considers a much wider varieties of attacking methods. But recent research focus that popularized by the deep learning community places strong emphasis on how the "imperceivable" perturbations on the normal inputs may cause dramatic mistakes by the deep learning with supposed super-human accuracy. This paper serves to give a comprehensive introduction to a range of aspects of the adversarial deep learning topic, including its foundations, typical attacking and defending strategies, and some extended studies.