Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs

• URL: http://arxiv.org/abs/2404.06025v2
• Date: Tue, 2 Jul 2024 15:48:49 GMT
• Title: Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs
• Authors: Zander W. Blasingame, Chen Liu,
• Abstract summary: Diffusion Morphs (DiM) are a recently proposed morphing attack that has achieved state-of-the-art performance for representation-based morphing attacks. We propose a greedy strategy on the iterative sampling process of DiM models which searches for an optimal step guided by an identity-based function. 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.
• Score: 2.0795007613453445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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