Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs
Zander W. Blasingame Chen Liu
Morphing attacks are an emerging threat to state-of-the-art Face Recognition
(FR) systems, which aim to create a single image that contains the biometric
information of multiple identities. Diffusion Morphs (DiM) are a recently
proposed morphing attack that has achieved state-of-the-art performance for
representation-based morphing attacks. However, none of the existing research
on DiMs have leveraged the iterative nature of DiMs and left the DiM model as a
black box, treating it no differently than one would a Generative Adversarial
Network (GAN) or Varational AutoEncoder (VAE). We propose a greedy strategy on
the iterative sampling process of DiM models which searches for an optimal step
guided by an identity-based heuristic function. We compare our proposed
algorithm against ten other state-of-the-art morphing algorithms using the
open-source SYN-MAD 2022 competition dataset. We find that our proposed
algorithm is unreasonably effective, fooling all of the tested FR systems with
an MMPMR of 100%, outperforming all other morphing algorithms compared.