Biometric technologies are widely adopted in security, legal, and financial systems. Face recognition can authenticate a person based on the unique facial features such as shape and texture. However, recent works have demonstrated the vulnerability of Face Recognition Systems (FRS) towards presentation attacks. Using spoofing (aka.,presentation attacks), a malicious actor can get illegitimate access to secure systems. This paper proposes a novel light-weight CNN framework to identify print/display, video and wrap attacks. The proposed robust architecture provides seamless liveness detection ensuring faster biometric authentication (1-2 seconds on CPU). Further, this also presents a newly created 2D spoof attack dataset consisting of more than 500 videos collected from 60 subjects. To validate the effectiveness of this architecture, we provide a demonstration video depicting print/display, video and wrap attack detection approaches. The demo can be viewed in the following link: https://rak.box.com/s/m1uf31fn5amtjp4mkgf1huh4ykfeibaa

翻译：生物识别技术已广泛应用于安全、司法和金融系统。人脸识别能够根据形状与纹理等独特面部特征对个体进行身份认证。然而，近期研究表明人脸识别系统易受呈现攻击的影响。攻击者通过欺骗手段（即呈现攻击）可非法访问安全系统。本文提出一种新颖的轻量级CNN框架，用于识别打印/显示屏攻击、视频攻击及面具攻击。该鲁棒架构提供无缝活体检测功能，可实现快速生物特征认证（CPU处理仅需1-2秒）。此外，本文还构建了包含60位受试者超500段视频的新型2D欺骗攻击数据集。为验证架构有效性，我们提供了展示打印/显示屏攻击、视频攻击及面具攻击检测方法的演示视频。演示视频可通过以下链接查看：https://rak.box.com/s/m1uf31fn5amtjp4mkgf1huh4ykfeibaa