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Real masks and spoof faces: On the masked face presentation attack detection.

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Face masks significantly challenge face recognition (FR) and pose serious threats to presentation attack detection (PAD) systems. This study introduces novel masked attacks, revealing vulnerabilities in current PAD algorithms and FR security.

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Area of Science:

  • Computer Science
  • Biometrics
  • Security Engineering

Background:

  • Face masks are crucial for COVID-19 transmission reduction, complicating face recognition (FR) systems.
  • Face presentation attack detection (PAD) is vital for FR system security.
  • The impact of masked attacks on PAD performance remains under-explored.

Purpose of the Study:

  • To investigate the effect of masked attacks on PAD performance.
  • To evaluate the vulnerability of FR systems to masked attacks.
  • To introduce novel masked attack methodologies reflecting real-world scenarios.

Main Methods:

  • Development of novel masked attacks using real face masks on presentations and subjects.
  • Evaluation of seven state-of-the-art PAD algorithms against masked attacks.
  • Assessment of FR system vulnerability under masked attack conditions.

Main Results:

  • Masked attacks pose a significant threat to the operation and security of FR systems.
  • Existing PAD algorithms show reduced performance when faced with masked attacks.
  • Real-world masked attacks present a serious security concern for biometric systems.

Conclusions:

  • The security of face recognition systems is compromised by masked attacks.
  • Further research is needed to develop robust PAD methods against masked threats.
  • Adapting FR and PAD systems for mask-wearing scenarios is essential for future security.