Abstract
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.
Abstract (translated)
在本文中，我们提出了一个新颖的进行面部变形攻击的方法，该方法利用最优特征点引导图像融合。当前的面部变形攻击可以分为基于地标和基于生成模型的方法。基于地标的 methods 使用几何变换根据平均地标扭曲面部区域，但通常会产生视觉质量较差的变形图像。基于生成的方法，使用生成模型将多个面部图像融合，可以实现更好的视觉质量，但通常无法生成能够有效逃避最先进面部识别系统（FRSs）的变形图像。我们提出的方法通过优化变形特征点并使用图卷积网络（GCNs）结合特征点和外观特征，克服了前方法的局限性。我们将面部特征点建模为二分图中的节点，并利用 GCNs 模拟其空间和结构关系。目标是在变形过程中捕捉面部形状的变异，并使面部外观特征在变形过程中得到准确的操作，从而生成高度逼真和视觉上忠实的外观图像。在两个公开数据集上的实验证明，我们的方法继承了前地标和生成模型的优势，生成的变形图像具有更高的质量，对最先进的 FRSs构成了更大的威胁。
URL
https://arxiv.org/abs/2401.16722
