  The susceptibility of deep neural networks to untrustworthy predictions,
including out-of-distribution (OOD) data and adversarial examples, still
prevent their widespread use in safety-critical applications. Most existing
methods either require a re-training of a given model to achieve robust
identification of adversarial attacks or are limited to out-of-distribution
sample detection only. In this work, we propose a geometric gradient analysis
(GGA) to improve the identification of untrustworthy predictions without
retraining of a given model. GGA analyzes the geometry of the loss landscape of
neural networks based on the saliency maps of their respective input. To
motivate the proposed approach, we provide theoretical connections between
gradients' geometrical properties and local minima of the loss function.
Furthermore, we demonstrate that the proposed method outperforms prior
approaches in detecting OOD data and adversarial attacks, including
state-of-the-art and adaptive attacks.
