用于人脸变形攻击的最优地标引导图像融合
Optimal-Landmark-Guided Image Blending for Face Morphing Attacks
AI总结：
该研究提出一种最优地标引导的人脸变形攻击方法，通过GCN结合地标与外观特征优化变形效果，兼具两类传统方法优势，生成的图像质量更高，对先进人脸识别系统威胁更大。
摘要：
本文提出了一种实施人脸变形攻击的新方法，该方法采用最优地标引导的图像融合技术。当前人脸变形攻击可分为基于地标和基于生成的两类方法。基于地标的方法根据平均地标使用几何变换扭曲面部区域，但生成的变形图像视觉质量往往较差。基于生成的方法采用生成模型融合多张人脸图像，虽能获得更好的视觉质量，但通常无法生成可有效规避最先进人脸识别系统（FRSs）的变形图像。我们提出的方法通过优化变形地标，并使用图卷积网络（GCNs）结合地标与外观特征，克服了以往方法的局限性。我们将人脸地标建模为一个完全连接的二分图中的节点，利用GCNs模拟其空间和结构关系。该方法旨在捕捉面部形状的变化，实现扭曲过程中面部外观特征的精准操控，从而生成高度逼真、视觉保真度高的变形人脸图像。在两个公开数据集上的实验证明，我们的方法兼具以往基于地标和基于生成方法的优势，生成的变形图像质量更高，对最先进的FRSs构成了更显著的威胁。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
