Towards everywhere reliable classification - A joint framework for adversarial robustness and out-of-distribution detection

• Applicant: Professor Dr. Matthias Hein
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 464101476

Adversarial robustness and out-of-distribution (OOD) detection have been treated separately so far. However, the separation of these problems is in our point of view artificial as they are inherently linked to each other. Advances in in adversarial robustness generalizing beyond the threat models used at training time seem possible only by going beyond the classical adversarial training framework proposed by Madry et al. and merging OOD detection and adversarial robustness in a single framework.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2298: Theoretical Foundations of Deep Learning