The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910694815825920 |
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| author | Neddo, Richard E. Blasingame, Zander W. Liu, Chen |
| author_facet | Neddo, Richard E. Blasingame, Zander W. Liu, Chen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios Neddo, Richard E. Blasingame, Zander W. Liu, Chen Computer Vision and Pattern Recognition 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. |
| title | The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.06559 |