  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.
