  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.
