HALO: Robust Out-of-Distribution Detection via Joint Optimisation

Abstract

Effective out-of-distribution (ODD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that ODD detection methods are vulnerable to adversarial attacks, potentially leading to critical failures in high-stakes applications. This discovery has motivated work on robust ODD detection methods that are capable of maintaining performance under various attack settings. Prior approaches have made progress on this problem but face a number of limitations: often only exhibiting robustness to attacks on ODD data or failing to maintain strong clean performance. In this work, we adapt an existing robust classification framework, ..