  This tutorial aims to introduce the fundamentals of adversarial robustness of
deep learning, presenting a well-structured review of up-to-date techniques to
assess the vulnerability of various types of deep learning models to
adversarial examples. This tutorial will particularly highlight
state-of-the-art techniques in adversarial attacks and robustness verification
of deep neural networks (DNNs). We will also introduce some effective
countermeasures to improve the robustness of deep learning models, with a
particular focus on adversarial training. We aim to provide a comprehensive
overall picture about this emerging direction and enable the community to be
aware of the urgency and importance of designing robust deep learning models in
safety-critical data analytical applications, ultimately enabling the end-users
to trust deep learning classifiers. We will also summarize potential research
directions concerning the adversarial robustness of deep learning, and its
potential benefits to enable accountable and trustworthy deep learning-based
data analytical systems and applications.
