  Face anti-spoofing (FAS) plays a vital role in securing face recognition
systems from presentation attacks. Existing multi-modal FAS methods rely on
stacked vanilla convolutions, which is weak in describing detailed intrinsic
information from modalities and easily being ineffective when the domain shifts
(e.g., cross attack and cross ethnicity). In this paper, we extend the central
difference convolutional networks (CDCN) \cite{yu2020searching} to a
multi-modal version, intending to capture intrinsic spoofing patterns among
three modalities (RGB, depth and infrared). Meanwhile, we also give an
elaborate study about single-modal based CDCN. Our approach won the first place
in "Track Multi-Modal" as well as the second place in "Track Single-Modal
(RGB)" of ChaLearn Face Anti-spoofing Attack Detection Challenge@CVPR2020
\cite{liu2020cross}. Our final submission obtains 1.02$\pm$0.59\% and
4.84$\pm$1.79\% ACER in "Track Multi-Modal" and "Track Single-Modal (RGB)",
respectively. The codes are available at{https://github.com/ZitongYu/CDCN}.
