Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
<|reference_start|>Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors: Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in real-world scenarios. In recent years, many OOD detectors have been developed, and even the benchmarking has been standardized, i.e. OpenOOD. The number of post-hoc detectors is growing fast and showing an option to protect a pre-trained classifier against natural distribution shifts, claiming to be ready for real-world scenarios. However, its efficacy in handling adversarial examples has been neglected in the majority of studies. This paper investigates the adversarial robustness of the 16 post-hoc detectors on several evasion attacks and discuss a roadmap towards adversarial defense in OOD detectors.<|reference_end|>
@article{lorenz2024deciphering,
    title={Deciphering the Definition of Adversarial Robustness for post-hoc OOD
  Detectors},
    author={Peter Lorenz, Mario Fernandez, Jens M"uller and Ullrich K"othe},
    journal={arXiv preprint arXiv:2406.15104},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15104},
    primaryClass={cs.CR cs.CV}
}