  Adversarial examples are inevitable on the road of pervasive applications of
deep neural networks (DNN). Imperceptible perturbations applied on natural
samples can lead DNN-based classifiers to output wrong prediction with fair
confidence score. It is increasingly important to obtain models with high
robustness that are resistant to adversarial examples. In this paper, we survey
recent advances in how to understand such intriguing property, i.e. adversarial
robustness, from different perspectives. We give preliminary definitions on
what adversarial attacks and robustness are. After that, we study
frequently-used benchmarks and mention theoretically-proved bounds for
adversarial robustness. We then provide an overview on analyzing correlations
among adversarial robustness and other critical indicators of DNN models.
Lastly, we introduce recent arguments on potential costs of adversarial
training which have attracted wide attention from the research community.
