  Detecting out-of-distribution (OOD) inputs is critical for safely deploying
deep learning models in the real world. Existing approaches for detecting OOD
examples work well when evaluated on benign in-distribution and OOD samples.
However, in this paper, we show that existing detection mechanisms can be
extremely brittle when evaluating on in-distribution and OOD inputs with
minimal adversarial perturbations which don't change their semantics. Formally,
we extensively study the problem of Robust Out-of-Distribution Detection on
common OOD detection approaches, and show that state-of-the-art OOD detectors
can be easily fooled by adding small perturbations to the in-distribution and
OOD inputs. To counteract these threats, we propose an effective algorithm
called ALOE, which performs robust training by exposing the model to both
adversarially crafted inlier and outlier examples. Our method can be flexibly
combined with, and render existing methods robust. On common benchmark
datasets, we show that ALOE substantially improves the robustness of
state-of-the-art OOD detection, with 58.4% AUROC improvement on CIFAR-10 and
46.59% improvement on CIFAR-100.
