  Semantic face image manipulation has received increasing attention in recent
years. StyleGAN-based approaches to face morphing are among the leading
techniques; however, they often suffer from noticeable blurring and artifacts
as a result of the uniform attention in the latent feature space. In this
paper, we propose to develop a transformer-based alternative to face morphing
and demonstrate its superiority to StyleGAN-based methods. Our contributions
are threefold. First, inspired by GANformer, we introduce a bipartite structure
to exploit long-range interactions in face images for iterative propagation of
information from latent variables to salient facial features. Special loss
functions are designed to support the optimization of face morphing. Second, we
extend the study of transformer-based face morphing to demorphing by presenting
an effective defense strategy with access to a reference image using the same
generator of MorphGANFormer. Such demorphing is conceptually similar to
unmixing of hyperspectral images but operates in the latent (instead of pixel)
space. Third, for the first time, we address a fundamental issue of
vulnerability-detectability trade-off for face morphing studies. It is argued
that neither doppelganger norrandom pair selection is optimal, and a Lagrangian
multiplier-based approach should be used to achieve an improved trade-off
between recognition vulnerability and attack detectability.
