🤖 AI Summary
This work addresses the often-overlooked degradation of predictive uncertainty quality caused by conventional adversarial training, which undermines selective classification performance despite improving model robustness. The study systematically reveals, for the first time, the adverse impact of adversarial training on uncertainty calibration and ranking. To mitigate this issue, the authors propose Evidence-based Adversarial Training (EV-AT), a novel approach grounded in evidential theory that jointly optimizes standard accuracy and uncertainty reliability in the Dirichlet parameter space. EV-AT employs an evidential loss combined with a robust evidential alignment loss to enforce consistency between predictions on clean and adversarial examples. Extensive experiments across multiple datasets and threat models demonstrate that EV-AT significantly outperforms existing methods, simultaneously enhancing both robust accuracy and selective classification performance, thereby advancing the Pareto frontier of the robustness–uncertainty trade-off.
📝 Abstract
Safety-critical applications require classifiers that are both robust and reliable. Adversarial training is a widely adopted defense for improving robustness in deep neural networks; however, its effect on the reliability of predictive uncertainty remains underexplored. We investigate this gap through the lens of selective classification, which has rarely been systematically analyzed alongside adversarial robustness. We introduce a unified benchmark for the robustness-uncertainty trade-off. It standardizes architectures, augmentations, threat models, and evaluation 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 robust accuracy while degrading uncertainty ranking, leading to poorer selective behavior. To address this, we propose Evidential Adversarial Training (EV-AT), which models uncertainty through a Dirichlet distribution and combines (i) an evidence-based loss promoting clean accuracy and reliable uncertainty with (ii) a robust evidence-alignment loss matching clean and adversarial predictions in log Dirichlet-parameter space. Extensive experiments show that EV-AT shifts the Pareto frontier of robustness-uncertainty trade-offs beyond prior state-of-the-art adversarial training methods. Our source code is publicly available at https://github.com/NicolasSournac/Robustness_Meets_Uncertainty.EV-AT.