Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

Safety-critical applications require classifiers that are bothrobust and reliable. Adversarial training is a widely adopted defense forimproving robustness in deep neural networks; however, its effect on thereliability of predictive uncertainty remains underexplored. We investi-gate this gap through the lens of selective classification, which has rarelybeen systematically analyzed alongside adversarial robustness. We in-troduce a unified benchmark for the robustness–uncertainty trade-off.It standardizes architectures, augmentations, threat models, and evalu-ation metrics across clean, adversarial, and common-corruption settings.Across a wide range of state-of-the-art adversarial training methods,we uncover a recurring failure mode: several approaches improve robustaccuracy while degrading uncertainty ranking, leading to poorer selec-tive behavior. To address this, we propose Evidential Adversarial Train-ing (EV-AT), which models uncertainty through a Dirichlet distributionand combines (i) an evidence-based loss promoting clean accuracy andreliable uncertainty with (ii) a robust evidence-alignment loss match-ing clean and adversarial predictions in log Dirichlet-parameter space.Extensive experiments show that EV-AT shifts the Pareto frontier ofrobustness–uncertainty trade-offs beyond prior state-of-the-art adversar-ial training methods. Our source code is publicly available at https://github.com/NicolasSournac/RobustnessMeetsUncertainty.EV-AT.