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The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios

Richard E. Neddo, Zander W. Blasingame, Chen Liu

TL;DR

This work investigates the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios and shows that increasing the Mated Morph Presentation Match Rate (MMPMR) can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%.

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.

The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios

TL;DR

This work investigates the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios and shows that increasing the Mated Morph Presentation Match Rate (MMPMR) can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%.

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.
Paper Structure (15 sections, 3 equations, 3 figures, 6 tables)

This paper contains 15 sections, 3 equations, 3 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Heterogeneous Morph Evaluation Scenarios
  4. Print-Scan Methodology
  5. Equipment and Settings
  6. Studied Morphing Attacks
  7. Experimental Setup
  8. Face Recognition Systems
  9. Datasets
  10. Metrics
  11. Results
  12. Vulnerability Study
  13. Detectability Study
  14. Conclusion
  15. Additional Results

Figures (3)

  • Figure 1: Artifacts on Morphed Image after Print-Scanning. Calculated and displayed by subtracting the digital image from the print-scanned image
  • Figure 2: Heterogeneous morph attack pipeline in a simulated real-world scenario
  • Figure 3: Image array displaying the importance of setting management during print-scanning to generate high-quality images