  With the increased deployment of face recognition systems in our daily lives,
face presentation attack detection (PAD) is attracting much attention and
playing a key role in securing face recognition systems. Despite the great
performance achieved by the hand-crafted and deep-learning-based methods in
intra-dataset evaluations, the performance drops when dealing with unseen
scenarios. In this work, we propose a dual-stream convolution neural networks
(CNNs) framework. One stream adapts four learnable frequency filters to learn
features in the frequency domain, which are less influenced by variations in
sensors/illuminations. The other stream leverages the RGB images to complement
the features of the frequency domain. Moreover, we propose a hierarchical
attention module integration to join the information from the two streams at
different stages by considering the nature of deep features in different layers
of the CNN. The proposed method is evaluated in the intra-dataset and
cross-dataset setups, and the results demonstrate that our proposed approach
enhances the generalizability in most experimental setups in comparison to
state-of-the-art, including the methods designed explicitly for domain
adaption/shift problems. We successfully prove the design of our proposed PAD
solution in a step-wise ablation study that involves our proposed learnable
frequency decomposition, our hierarchical attention module design, and the used
loss function. Training codes and pre-trained models are publicly released
