使用最小的End-2-End神经网络的基于注意力的RGB图像人脸防欺骗

面部防欺骗旨在识别真实的面部和伪造的面部，并在安全敏感的应用程序，活动检测，指纹识别等方面引起了高度关注。在本文中，我们通过提出两个卷积神经网络的端到端系统来解决反欺骗问题。..

Attention-Based Face AntiSpoofing of RGB Images, using a Minimal End-2-End Neural Network

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.