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%.

翻译：如今，呈现攻击检测是一个十分活跃的研究领域。当前最先进的技术中，已有多个数据库利用从视频中提取的图像构建而成。发现的主要问题之一是，许多数据库图像质量低、尺寸小，无法代表真实远程生物识别系统中的操作场景。目前，这些图像通常由智能手机以高质量和更高分辨率捕获。为增加图像质量的多样性，本工作基于开放获取的Flickr图像构建了一个新的呈现攻击检测数据库，命名为"Flickr-PAD"。我们手工制作的新数据库提供了高质量打印场景和屏幕场景。这将有助于研究人员在更广泛的数据库上比较新方法与现有算法。该数据库将向其他研究者开放使用。采用留一法协议对基于MobileNet-V3（小规模和大规模）和EfficientNet-B0的三个呈现攻击检测模型进行训练与评估。其中，MobileNet-V3大规模模型取得了最佳结果，BPCER10为7.08%，BPCER20为11.15%。