Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models. DBU models predict the parameters of a Dirichlet distribution to provide fast, high-quality uncertainty estimates alongside with class predictions. In this work, we present the first large-scale, in-depth study of the robustness of DBU models under adversarial attacks. Our results suggest that uncertainty estimates of DBU models are not robust w.r.t. three important tasks: (1) indicating correctly and wrongly classified samples; (2) detecting adversarial examples; and (3) distinguishing between in-distribution (ID) and out-of-distribution (OOD) data. Additionally, we explore the first approaches to make DBU models more robust. While adversarial training has a minor effect, our median smoothing based approach significantly increases robustness of DBU models.
翻译:基于二氧化二氮的不确定性模型(DBU)是最近有希望的、有前途的一类不确定性模型。DBU模型预测了dirichlet分布的参数,以提供快速、高质量的不确定性估计数,同时提供等级预测。在这项工作中,我们提出了首次大规模深入研究在对抗性攻击情况下使用二硝基二苯的不确定性模型的稳健性。我们的结果表明,对二硝基二苯模型的不确定性估计数并不可靠,这三项重要任务:(1) 指出正确和错误的分类样本;(2) 发现对抗性实例;(3) 区分分配(ID)和分配(OOD)数据。此外,我们探索了第一种方法,使二硝基二氮基二苯模型更加稳健。虽然对抗性培训效果不大,但我们基于中位的平滑法方法大大提高了二硝基二氮模型的稳健性。