In this paper, we propose a novel approach for conducting face morphing attacks, which utilizes optimal-landmark-guided image blending. Current face morphing attacks can be categorized into landmark-based and generation-based approaches. Landmark-based methods use geometric transformations to warp facial regions according to averaged landmarks but often produce morphed images with poor visual quality. Generation-based methods, which employ generation models to blend multiple face images, can achieve better visual quality but are often unsuccessful in generating morphed images that can effectively evade state-of-the-art face recognition systems~(FRSs). Our proposed method overcomes the limitations of previous approaches by optimizing the morphing landmarks and using Graph Convolutional Networks (GCNs) to combine landmark and appearance features. We model facial landmarks as nodes in a bipartite graph that is fully connected and utilize GCNs to simulate their spatial and structural relationships. The aim is to capture variations in facial shape and enable accurate manipulation of facial appearance features during the warping process, resulting in morphed facial images that are highly realistic and visually faithful. Experiments on two public datasets prove that our method inherits the advantages of previous landmark-based and generation-based methods and generates morphed images with higher quality, posing a more significant threat to state-of-the-art FRSs.

翻译：在本文中，我们提出了一种新颖的人脸形变攻击方法，该方法利用最优地标引导的图像融合技术。当前的人脸形变攻击方法可分为基于地标和基于生成模型两类。基于地标的方法通过平均地标对脸部区域进行几何变换扭曲，但生成的形变图像视觉质量较差。基于生成模型的方法使用生成模型融合多张人脸图像，虽能获得更优视觉质量，但往往无法生成有效逃避最先进人脸识别系统（FRSs）的形变图像。本文提出的方法通过优化形变地标并利用图卷积网络（GCNs）融合地标与外观特征，克服了先前方法的局限性。我们将面部地标建模为全连接二分图中的节点，利用GCNs模拟其空间与结构关系，旨在捕捉面部形状变化，并在扭曲过程中实现面部外观特征的精确操控，从而生成高度逼真且视觉保真的形变人脸图像。在两个公开数据集上的实验证明，本方法继承了基于地标与基于生成模型两类方法的优势，生成质量更高的形变图像，对最先进的人脸识别系统构成更大威胁。