Title: Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?. Abstract: Robustness to adversarial perturbations and accurate uncertainty estimation are crucial for reliable application of deep learning in real world settings. Dirichlet-based uncertainty (DBU) models are a family of models that predict the parameters of a Dirichlet distribution (instead of a categorical one) and promise to signal when not to trust their predictions. Untrustworthy predictions are obtained on unknown or ambiguous samples and marked with a high uncertainty by the models.

In this work, we show that DBU models with standard training are not robust w.r.t. three important tasks in the field of uncertainty estimation. First, we evaluate how useful the uncertainty estimates are to (1) indicate correctly classified samples. Our results show that while they are a good indicator on unperturbed data, performance on perturbed data decreases dramatically. (2) We evaluate if uncertainty estimates are able to detect adversarial examples that try to fool classification. It turns out that uncertainty estimates are able to detect FGSM attacks but not able to detect PGD attacks. We further evaluate the reliability of DBU models on the task of (3) distinguishing between in-distribution (ID) and out-of-distribution (OOD) data. To this end, we present the first study of certifiable robustness for DBU models. Furthermore, we propose novel uncertainty attacks that fool models into assigning high confidence to OOD data and low confidence to ID data, respectively. 
Both approaches show that detecting OOD samples and distinguishing between ID-data and OOD-data is not robust.

Based on our results, we explore the first approaches to make DBU models more robust. We use adversarial training procedures based on label attacks, uncertainty attacks, or random noise and demonstrate how they affect robustness of DBU models on ID data and OOD data.