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Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function.

Waldir R Almeida1, Fernanda A Andaló1, Rafael Padilha1

  • 1Institute of Computing, University of Campinas, Campinas, São Paulo, Brazil.

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Summary
This summary is machine-generated.

This study introduces a new software-based method for detecting presentation attacks on face authentication systems, crucial for mobile device security. The approach uses a data-driven technique and a new dataset to improve accuracy in real-world scenarios.

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Area of Science:

  • Computer Science
  • Biometrics
  • Security Engineering

Background:

  • Presentation attacks, like using printed images to fool face recognition, threaten biometric authentication effectiveness.
  • Fast device unlocking and mobile hardware constraints pose challenges for robust face authentication security.

Purpose of the Study:

  • To develop and evaluate a purely software-based, data-driven method for detecting presentation attacks in face authentication systems.
  • To address the limitations of existing datasets and approaches by proposing a new method and dataset tailored for mobile devices.

Main Methods:

  • A data-driven approach using multi-resolution patches and a multi-objective loss function for presentation attack detection.
  • Analysis using user-disjoint and cross-factor protocols to evaluate method performance and understand problem complexities.
  • Development of an adaptive method leveraging user data and device characteristics for enhanced efficacy.

Main Results:

  • The proposed method demonstrates competitive results in presentation attack detection.
  • Analysis highlights issues with current datasets and provides conceptual understanding of detection challenges.
  • The adaptive strategy enhances discriminability by considering user and device specifics.

Conclusions:

  • A novel, purely software-based method effectively detects presentation attacks in face authentication for mobile devices.
  • The new dataset and adaptive approach improve security and address real-world lighting variations.
  • This research contributes to more robust and secure biometric authentication systems on mobile platforms.