Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning

Quantifying uncertainty to reject out-of-distribution/adversarially attacked inputs in crucial for deploying deep learning models in the real-world. Our proposed method, C-EDL, boosts robustness in Evidential Deep Learning by detecting conflict from input transformations, improving OOD and adversarial detection without retraining, while keeping high accuracy and low overhead.