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The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios (2404.06559v2)

Published 9 Apr 2024 in cs.CV

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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Authors (3)
  1. Richard E. Neddo (2 papers)
  2. Zander W. Blasingame (7 papers)
  3. Chen Liu (206 papers)

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