  Face anti-spoofing aims at identifying the real face, as well as the fake
one, and gains a high attention in security-sensitive applications, liveness
detection, fingerprinting, and so on. In this paper, we address the
anti-spoofing problem by proposing two end-to-end systems of convolutional
neural networks. One model is developed based on the EfficientNet B0 network
which has been modified in the final dense layers. The second one, is a very
light model of the MobileNet V2, which has been contracted, modified and
retrained efficiently on the data being created based on the Rose-Youtu
dataset, for this purpose. The experiments show that, both of the proposed
architectures achieve remarkable results on detecting the real and fake images
of the face input data. The experiments clearly show that the heavy-weight
model could be efficiently employed in server-side implementations, whereas the
low-weight model could be easily implemented on the hand-held devices and both
perform perfectly well using merely RGB input images.
