  In pursuit of consolidating the face verification systems, prior face
anti-spoofing studies excavate the hidden cues in original images to
discriminate real persons and diverse attack types with the assistance of
auxiliary supervision. However, limited by the following two inherent
disturbances in their training process: 1) Complete facial structure in a
single image. 2) Implicit subdomains in the whole dataset, these methods are
prone to stick on memorization of the entire training dataset and show
sensitivity to nonhomologous domain distribution. In this paper, we propose
Structure Destruction Module and Content Combination Module to address these
two imitations separately. The former mechanism destroys images into patches to
construct a non-structural input, while the latter mechanism recombines patches
from different subdomains or classes into a mixup construct. Based on this
splitting-and-splicing operation, Local Relation Modeling Module is further
proposed to model the second-order relationship between patches. We evaluate
our method on extensive public datasets and promising experimental results to
demonstrate the reliability of our method against state-of-the-art competitors.
