Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs
<|reference_start|>Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs: 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.<|reference_end|>
@article{blasingame2024greedy-dim:,
    title={Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs},
    author={Zander W. Blasingame, Chen Liu},
    journal={2024 IEEE International Joint Conference on Biometrics (IJCB),
  Buffalo, NY, USA, 2024, pp. 1-11},
    year={2024},
    doi={10.1109/IJCB62174.2024.10744517},
    archivePrefix={arXiv},
    eprint={2404.06025},
    primaryClass={cs.CV cs.AI}
}