A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Understand

Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications.

• However, deep neural networks with the softmax classifier are known to produce highly overconfident posterior distributions even for such abnormal samples.
• In this paper, we propose a simple yet effective method for detecting any abnormal samples, which is applicable to any pre-trained softmax neural classifier.
• We obtain the class conditional Gaussian distributions with respect to (low- and upper-level) features of the deep models under Gaussian discriminant analysis, which result in a confidence score based on the Mahalanobis distance.