no code implementations • 1 Jun 2022 • Jiaxin Cheng, Mohamed Hussein, Jay Billa, Wael AbdAlmageed
The growing number of adversarial attacks in recent years gives attackers an advantage over defenders, as defenders must train detectors after knowing the types of attacks, and many models need to be maintained to ensure good performance in detecting any upcoming attacks.
no code implementations • ICCV 2021 • Mohammad Rostami, Leonidas Spinoulas, Mohamed Hussein, Joe Mathai, Wael Abd-Almageed
Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research.
1 code implementation • 12 Jun 2020 • Leonidas Spinoulas, Hengameh Mirzaalian, Mohamed Hussein, Wael Abd-Almageed
Our evaluation compares different combination of the new sensing modalities to legacy data from one of our collections as well as the public LivDet2015 dataset, showing the superiority of the new sensing modalities in most cases.
no code implementations • 12 Jun 2020 • Leonidas Spinoulas, Mohamed Hussein, David Geissbühler, Joe Mathai, Oswin G. Almeida, Guillaume Clivaz, Sébastien Marcel, Wael Abd-Almageed
In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources.
no code implementations • 6 Jun 2019 • Hengameh Mirzaalian, Mohamed Hussein, Wael Abd-Almageed
To examine the effect of the presence of an unseen attack, we apply a leave-one-attack out strategy.