The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios
<|reference_start|>The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios: 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.<|reference_end|>
@article{neddo2024the,
    title={The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios},
    author={Richard E. Neddo, Zander W. Blasingame, Chen Liu},
    journal={2024 IEEE International Joint Conference on Biometrics (IJCB),
  Buffalo, NY, USA, 2024, pp. 1-10},
    year={2024},
    doi={10.1109/IJCB62174.2024.10744441},
    archivePrefix={arXiv},
    eprint={2404.06559},
    primaryClass={cs.CV}
}