  Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most
state-of-the-art FAS methods 1) rely on stacked convolutions and
expert-designed network, which is weak in describing detailed fine-grained
information and easily being ineffective when the environment varies (e.g.,
different illumination), and 2) prefer to use long sequence as input to extract
dynamic features, making them difficult to deploy into scenarios which need
quick response. Here we propose a novel frame level FAS method based on Central
Difference Convolution (CDC), which is able to capture intrinsic detailed
patterns via aggregating both intensity and gradient information. A network
built with CDC, called the Central Difference Convolutional Network (CDCN), is
able to provide more robust modeling capacity than its counterpart built with
vanilla convolution. Furthermore, over a specifically designed CDC search
space, Neural Architecture Search (NAS) is utilized to discover a more powerful
network structure (CDCN++), which can be assembled with Multiscale Attention
Fusion Module (MAFM) for further boosting performance. Comprehensive
experiments are performed on six benchmark datasets to show that 1) the
proposed method not only achieves superior performance on intra-dataset testing
(especially 0.2% ACER in Protocol-1 of OULU-NPU dataset), 2) it also
generalizes well on cross-dataset testing (particularly 6.5% HTER from
CASIA-MFSD to Replay-Attack datasets). The codes are available at
\href{https://github.com/ZitongYu/CDCN}{https://github.com/ZitongYu/CDCN}.
