  In the past few years, significant progress has been made on deep neural
networks (DNNs) in achieving human-level performance on several long-standing
tasks. With the broader deployment of DNNs on various applications, the
concerns over their safety and trustworthiness have been raised in public,
especially after the widely reported fatal incidents involving self-driving
cars. Research to address these concerns is particularly active, with a
significant number of papers released in the past few years. This survey paper
conducts a review of the current research effort into making DNNs safe and
trustworthy, by focusing on four aspects: verification, testing, adversarial
attack and defence, and interpretability. In total, we survey 202 papers, most
of which were published after 2017.

Machine Learning;Artificial Intelligence