Nowadays, Presentation Attack Detection is a very active research area.
Several databases are constituted in the state-of-the-art using images
extracted from videos. One of the main problems identified is that many
databases present a low-quality, small image size and do not represent an
operational scenario in a real remote biometric system. Currently, these images
are captured from smartphones with high-quality and bigger resolutions. In
order to increase the diversity of image quality, this work presents a new PAD
database based on open-access Flickr images called: "Flickr-PAD". Our new
hand-made database shows high-quality printed and screen scenarios. This will
help researchers to compare new approaches to existing algorithms on a wider
database. This database will be available for other researchers. A
leave-one-out protocol was used to train and evaluate three PAD models based on
MobileNet-V3 (small and large) and EfficientNet-B0. The best result was reached
with MobileNet-V3 large with BPCER10 of 7.08% and BPCER20 of 11.15%.

本研究提出了一种基于 Flickr 图像的新型 PAD 数据库：Flickr-PAD，利用该数据库进行训练和评估可以得到比现有算法更好的结果，该方法使用了 MobileNet-V3 和 EfficientNet-B0 模型， BPCER10 为 7.08％，BPCER20 为 11.15％。