论文标题
可靠的面部变形攻击检测在直接边境控制场景中，图像分辨率和捕获距离的变化
Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance
论文作者
论文摘要
面部识别系统（FRS）容易受到直接和间接执行的各种攻击的影响。在这些攻击中，面对的变形攻击在欺骗自动FRS和人类观察员方面具有很高的潜力，并表明了严重的安全威胁，尤其是在边境控制场景中。这项工作提出了面部变形攻击检测，尤其是在自动边界控制（ABC）场景中。我们提出了一种基于从六个不同的预训练的深卷积神经网络（CNN）计算出的深层特征的球形插值和分层融合的新型差异弹药（D-MAD）算法。通过考虑自动边界控制（ABC）门的真实情况，基于新生成的面部变形数据集（SCFACE-MORPH）进行了广泛的实验。实验协议旨在基准对不同摄像机分辨率的提议和最先进（SOTA）D-MAD技术并捕获距离。获得的结果表明，与现有方法相比，所提出的D-MAD方法的出色性能。
Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical interpolation and hierarchical fusion of deep features computed from six different pre-trained deep Convolutional Neural Networks (CNNs). Extensive experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset by considering the real-life scenario of Automatic Border Control (ABC) gates. Experimental protocols are designed to benchmark the proposed and state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and capture distances. Obtained results have indicated the superior performance of the proposed D-MAD method compared to the existing methods.
