  In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it
is imperative that DNNs provide inference robust to external perturbations -
both intentional and unintentional. Although the resilience of DNNs to
intentional and unintentional perturbations has been widely investigated, a
unified vision of these inherently intertwined problem domains is still
missing. In this work, we fill this gap by providing a survey of the state of
the art and highlighting the similarities of the proposed approaches.We also
analyze the research challenges that need to be addressed to deploy resilient
and secure DNNs. As there has not been any such survey connecting the
resilience of DNNs to intentional and unintentional perturbations, we believe
this work can help advance the frontier in both domains by enabling the
exchange of ideas between the two communities.
