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Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent

Kacper Kubicki1, Paweł Kapusta1, Krzysztof Ślot1

  • 1Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18/22, 90-001 Łódź, Poland.

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Summary
This summary is machine-generated.

This study introduces a novel convolutional phoneme classifier for presentation attack detection in speaker verification. The method achieves high accuracy on simplified networks, enabling efficient verification on mobile devices.

Keywords:
biometricsdeep neural networksmel-spectrogramphoneme classificationpresentation attack detection

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Area of Science:

  • Speech Processing
  • Biometrics
  • Machine Learning

Background:

  • Speaker verification systems face presentation attacks.
  • Challenge-response schemes are used for detection.
  • Resource-limited hardware poses challenges for complex models.

Purpose of the Study:

  • To develop a novel approach for training convolutional phoneme classifiers.
  • To enable efficient presentation attack detection on resource-limited devices.
  • To improve phoneme recognition accuracy in simplified neural networks.

Main Methods:

  • Utilized Deep Convolutional Neural Networks on Mel-Spectrograms.
  • Developed a new training set construction method focusing on central phoneme articulation intervals.
  • Employed bagging for ensembling simple classifiers.

Main Results:

  • Achieved up to 76% accuracy in a 39-phoneme recognition task with simplified networks.
  • Reduced within-class data scatter by optimizing training data selection.
  • Ensembling improved accuracy by 2-3%, reaching 23% Phoneme Error Rate.

Conclusions:

  • The proposed method allows reliable presentation attack detection on resource-limited hardware.
  • Simplified yet effective neural architectures can achieve competitive performance.
  • This approach is suitable for mobile and embedded biometric systems.