Security Matters: A Survey on Adversarial Machine Learning
It addresses the problem of security vulnerabilities in machine learning systems for researchers and practitioners, but is incremental as it synthesizes existing knowledge without novel contributions.
This survey provides a comprehensive introduction to adversarial machine learning, covering its foundations, typical attack and defense strategies, and extended studies, without presenting new experimental results or numerical findings.
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.