The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios

• URL: http://arxiv.org/abs/2404.06559v2
• Date: Tue, 3 Sep 2024 01:57:04 GMT
• Title: The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios
• Authors: Richard E. Neddo, Zander W. Blasingame, Chen Liu,
• Abstract summary: We investigate the impact of print-scanning on morphing attack detection through a series of evaluations. Experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. 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%.
• Score: 1.9035583634286277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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