论文标题
面部变形：欺骗面部识别系统很简单！
Face Morphing: Fooling a Face Recognition System Is Simple!
论文作者
论文摘要
最先进的面部识别方法（FR）方法在预测两个面是否属于相同身份方面显示出了显着的结果，根据协议的难度，准确性在92％至100％之间。但是，当暴露于变形的面孔时，精度会大大下降，特别是看起来类似于两个身份。为了产生变形的面孔，我们将简单的预处理模型集成到生成对抗网络（GAN）中，并修改了面部变形的几个损失功能。与以前的作品相反，我们的方法和分析不仅限于具有相同种族和性别的额叶面孔。我们的定性和定量结果肯定，即使在不受约束的情况下，我们的方法也会在两个面之间实现无缝变化。尽管使用了更简单的FR模型进行面部变形的功能，但我们证明，即使是最近的FR系统也很难区分变形的面部与两种身份仅获得55-70％的精度。此外，我们还提供了有关了解FR系统如何使其特别容易面临变形攻击的进一步见解。
State-of-the-art face recognition (FR) approaches have shown remarkable results in predicting whether two faces belong to the same identity, yielding accuracies between 92% and 100% depending on the difficulty of the protocol. However, the accuracy drops substantially when exposed to morphed faces, specifically generated to look similar to two identities. To generate morphed faces, we integrate a simple pretrained FR model into a generative adversarial network (GAN) and modify several loss functions for face morphing. In contrast to previous works, our approach and analyses are not limited to pairs of frontal faces with the same ethnicity and gender. Our qualitative and quantitative results affirm that our approach achieves a seamless change between two faces even in unconstrained scenarios. Despite using features from a simpler FR model for face morphing, we demonstrate that even recent FR systems struggle to distinguish the morphed face from both identities obtaining an accuracy of only 55-70%. Besides, we provide further insights into how knowing the FR system makes it particularly vulnerable to face morphing attacks.
