  The Mahalanobis distance-based confidence score, a recently proposed anomaly
detection method for pre-trained neural classifiers, achieves state-of-the-art
performance on both out-of-distribution (OoD) and adversarial examples
detection. This work analyzes why this method exhibits such strong performance
in practical settings while imposing an implausible assumption; namely, that
class conditional distributions of pre-trained features have tied covariance.
Although the Mahalanobis distance-based method is claimed to be motivated by
classification prediction confidence, we find that its superior performance
stems from information not useful for classification. This suggests that the
reason the Mahalanobis confidence score works so well is mistaken, and makes
use of different information from ODIN, another popular OoD detection method
based on prediction confidence. This perspective motivates us to combine these
two methods, and the combined detector exhibits improved performance and
robustness. These findings provide insight into the behavior of neural
classifiers in response to anomalous inputs.
