  Morphing is the process of combining two or more subjects in an image in
order to create a new identity which contains features of both individuals.
Morphed images can fool Facial Recognition Systems (FRS) into falsely accepting
multiple people, leading to failures in national security. As morphed image
synthesis becomes easier, it is vital to expand the research community's
available data to help combat this dilemma. In this paper, we explore
combination of two methods for morphed image generation, those of geometric
transformation (warping and blending to create morphed images) and photometric
perturbation. We leverage both methods to generate high-quality adversarially
perturbed morphs from the FERET, FRGC, and FRLL datasets. The final images
retain high similarity to both input subjects while resulting in minimal
artifacts in the visual domain. Images are synthesized by fusing the wavelet
sub-bands from the two look-alike subjects, and then adversarially perturbed to
create highly convincing imagery to deceive both humans and deep morph
detectors.
