Taming Self-Supervised Learning for Presentation Attack Detection: In-Image De-Folding and Out-of-Image De-Mixing
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by
Haozhe Liu, Zhe Kong, Raghavendra Ramachandra, Feng Liu, Linlin Shen, Christoph Busch
2021
Abstract
Biometric systems are vulnerable to the Presentation Attacks (PA) performed
using various Presentation Attack Instruments (PAIs). Even though there are
numerous Presentation Attack Detection (PAD) techniques based on both deep
learning and hand-crafted features, the generalization of PAD for unknown PAI
is still a challenging problem. The common problem with existing deep
learning-based PAD techniques is that they may struggle with local optima,
resulting in weak generalization against different PAs. In this work, we
propose to use self-supervised learning to find a reasonable initialization
against local trap, so as to improve the generalization ability in detecting
PAs on the biometric system.The proposed method, denoted as IF-OM, is based on
a global-local view coupled with De-Folding and De-Mixing to derive the
task-specific representation for PAD.During De-Folding, the proposed technique
will learn region-specific features to represent samples in a local pattern by
explicitly maximizing cycle consistency. While, De-Mixing drives detectors to
obtain the instance-specific features with global information for more
comprehensive representation by maximizing topological consistency. Extensive
experimental results show that the proposed method can achieve significant
improvements in terms of both face and fingerprint PAD in more complicated and
hybrid datasets, when compared with the state-of-the-art methods. Specifically,
when training in CASIA-FASD and Idiap Replay-Attack, the proposed method can
achieve 18.60% Equal Error Rate (EER) in OULU-NPU and MSU-MFSD, exceeding
baseline performance by 9.54%. Code will be made publicly available.
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