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Found 5 papers, 1 papers with code
Attack-Agnostic Adversarial Detection
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.
Detection and Continual Learning of Novel Face Presentation Attacks
Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research.
Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset
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.
Multispectral Biometrics System Framework: Application to Presentation Attack Detection
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.
On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks
To examine the effect of the presence of an unseen attack, we apply a leave-one-attack out strategy.
