  Face-morphing attacks are a growing concern for biometric researchers, as
they can be used to fool face recognition systems (FRS). These attacks can be
generated at the image level (supervised) or representation level
(unsupervised). Previous unsupervised morphing attacks have relied on
generative adversarial networks (GANs). More recently, researchers have used
linear interpolation of StyleGAN-encoded images to generate morphing attacks.
In this paper, we propose a new method for generating high-quality morphing
attacks using StyleGAN disentanglement. Our approach, called MLSD-GAN,
spherically interpolates the disentangled latents to produce realistic and
diverse morphing attacks. We evaluate the vulnerability of MLSD-GAN on two
deep-learning-based FRS techniques. The results show that MLSD-GAN poses a
significant threat to FRS, as it can generate morphing attacks that are highly
effective at fooling these systems.
