The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios

Richard E. Neddo, Zander W. Blasingame, Chen Liu
4/9/2024
cs.CV

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Abstract

Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vulnerabilities in FRs. These systems are particularly susceptible to attacks when the morphs are subjected to print-scanning to mask the artifacts generated during the morphing process. We investigate the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios. Our experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. Furthermore, when a Single-image Morphing Attack Detection (S-MAD) algorithm is not trained to detect print-scanned morphs the Morphing Attack Classification Error Rate (MACER) can increase by up to 96.12%, indicating significant vulnerability.

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Cite this paper

@article{neddo2024impact,
  title  = {The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios},
  author = {Richard E. Neddo and Zander W. Blasingame and Chen Liu},
  year   = {2024},
  eprint = {2404.06559},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.06559v2}
}

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