Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A
  Survey
Naveen Karunanayake Ravin Gunawardena Suranga Seneviratne Sanjay Chawla
Deep neural networks (DNNs) deployed in real-world applications can encounter
out-of-distribution (OOD) data and adversarial examples. These represent
distinct forms of distributional shifts that can significantly impact DNNs'
reliability and robustness. Traditionally, research has addressed OOD detection
and adversarial robustness as separate challenges. This survey focuses on the
intersection of these two areas, examining how the research community has
investigated them together. Consequently, we identify two key research
directions: robust OOD detection and unified robustness. Robust OOD detection
aims to differentiate between in-distribution (ID) data and OOD data, even when
they are adversarially manipulated to deceive the OOD detector. Unified
robustness seeks a single approach to make DNNs robust against both adversarial
attacks and OOD inputs. Accordingly, first, we establish a taxonomy based on
the concept of distributional shifts. This framework clarifies how robust OOD
detection and unified robustness relate to other research areas addressing
distributional shifts, such as OOD detection, open set recognition, and anomaly
detection. Subsequently, we review existing work on robust OOD detection and
unified robustness. Finally, we highlight the limitations of the existing work
and propose promising research directions that explore adversarial and OOD
inputs within a unified framework.