On the Adversarial Robustness of Learning-based Conformal Novelty Detection

ArXi:2510.00463v4 Announce Type: replace-cross This paper studies the adversarial robustness of conformal novelty detection. In particular, we focus on two powerful learning-based frameworks that come with finite-sample false discovery rate (FDR) control: one is AdaDetect (by Marandon, 2024) that is based on the positive-unlabeled classifier, and the other is a one-class classifier-based approach (by Bates, 2023). While they provide rigorous statistical guarantees under benign conditions, their behavior under adversarial perturbations remains underexplored.