CONF Kotwal_IJCB-2_2024/IDIAP Assessing the Reliability of Biometric Authentication on Virtual Reality Devices Kotwal, Ketan Ozbulak, Gokhan Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/papers/2024/Kotwal_IJCB-2_2024.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/Kotwal_Idiap-RR-04-2024 Related documents Proceedings of IEEE International Joint Conference on Biometrics 2024 Recent developments in Virtual Reality (VR) headsets have unlocked a plethora of innovative use-cases, many of which were previously unimaginable. However, as these use-cases, such as personalized immersive experiences, necessitate user authentication, ensuring robustness and resistance to spoofing attacks becomes imperative. The absence of appropriate dataset has constrained our understanding and assessment of VR devices’ susceptibility to presentation attacks. To address this research gap, we introduce VRBiom: a new periocular video dataset acquired from a VR headset (Meta Quest Pro), comprising 900 genuine and 1104 presentation attack videos, each spanning 10 seconds. The bona-fide videos consist of variations in terms of gaze and glasses; while the attacks are constructed with 6 different types of instruments. Additionally, we evaluate the performance of two prominent CNN architectures trained using various configurations for detecting presentation attacks in the newly created VRBiom dataset. Our benchmarking on VRBiom reveals the presence of spoofing threats in VR headsets. While baseline models exhibit considerable efficacy in attack detection, substantial scope exists for improvement in detecting attacks on periocular videos. Our dataset will be a useful resource for researchers aiming to enhance the security and reliability of VR-based authentication systems. REPORT Kotwal_Idiap-RR-04-2024/IDIAP Assessing the Reliability of Biometric Authentication on Virtual Reality Devices Kotwal, Ketan Ozbulak, Gokhan Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/reports/2024/Kotwal_Idiap-RR-04-2024.pdf PUBLIC Idiap-RR-04-2024 2024 Idiap July 2024 Recent developments in Virtual Reality (VR) headsets have unlocked a plethora of innovative use-cases, many of which were previously unimaginable. However, as these use-cases, such as personalized immersive experiences, necessitate user authentication, ensuring robustness and resistance to spoofing attacks becomes imperative. The absence of appropriate dataset has constrained our understanding and assessment of VR devices’ vulnerability to presentation attacks. To address this research gap, we introduce a new periocular video dataset acquired from a VR headset (Meta Quest Pro), comprising 900 genuine and 996 presentation attack videos, each spanning 10 seconds. The bona-fide videos consist of variations in terms of gaze and glasses; while the attacks are constructed with 6 different types of instruments. Additionally, we evaluate the performance of two prominent CNN architectures trained using various configurations for detecting presentation attacks in the newly created dataset, VRPAD. Our benchmarking on VRPAD reveals the presence of spoofing threats in VR headsets. While baseline models exhibit considerable efficacy in attack detection, substantial scope exists for improvement in detecting attacks on periocular videos. Our dataset will be a useful resource for researchers aiming to enhance the security and reliability of VR-based authentication systems.