  The robustness and generalization ability of Presentation Attack Detection
(PAD) methods is critical to ensure the security of Face Recognition Systems
(FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and
it is hard to predict the Presentation Attack Instrument (PAI) species that
will be used by the attacker. Existing PAD methods are highly dependent on the
limited training set and cannot generalize well to unknown PAI species. Unlike
this specific PAD task, other face related tasks trained by huge amount of real
faces (e.g. face recognition and attribute editing) can be effectively adopted
into different application scenarios. Inspired by this, we propose to trade
position of PAD and face related work in a face system and apply the free
acquired prior knowledge from face related tasks to solve face PAD, so as to
improve the generalization ability in detecting PAs. The proposed method, first
introduces task specific features from other face related task, then, we design
a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such
features to adapt to PAD task. Finally, face PAD is achieved by using the
hierarchical features from a CNN-based PA detector and the re-mapped features.
The experimental results show that the proposed method can achieve significant
improvements in the complicated and hybrid datasets, when compared with the
state-of-the-art methods. In particular, when training on the datasets
OULU-NPU, CASIA-FASD, and Idiap Replay-Attack, we obtain HTER (Half Total Error
Rate) of 5.48% for the testing dataset MSU-MFSD, outperforming the baseline by
7.39%.
