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Voice Presentation Attack Detection Using Convolutional Neural Networks

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Handbook of Biometric Anti-Spoofing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Current state-of-the-art automatic speaker verification (ASV) systems are prone to spoofing. The security and reliability of ASV systems can be threatened by different types of spoofing attacks using voice conversion, synthetic speech, or recorded passphrase. It is therefore essential to develop countermeasure techniques which can detect such spoofed speech. Inspired by the success of deep learning approaches in various classification tasks, this work presents an in-depth study of convolutional neural networks (CNNs) for spoofing detection in automatic speaker verification (ASV) systems. Specifically, we have compared the use of three different CNNs architectures: AlexNet, CNNs with max-feature-map activation, and an ensemble of standard CNNs for developing spoofing countermeasures, and discussed their potential to avoid overfitting due to small amounts of training data that is usually available in this task. We used popular deep learning toolkits for the system implementation and have released the implementation code of our methods publicly. We have evaluated the proposed countermeasure systems for detecting replay attacks on recently released spoofing corpora ASVspoof 2017, and also provided in-depth visual analyses of CNNs to aid for future research in this area.

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Notes

  1. 1.

    http://www.asvspoof.org/.

  2. 2.

    https://au.mathworks.com/help/nnet/ref/alexnet.html.

  3. 3.

    https://www.tensorflow.org.

  4. 4.

    https://www.pytorch.org.

  5. 5.

    https://anaconda.org/anaconda/python.

  6. 6.

    https://github.com/insikk/Grad-CAM-tensorflow.

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Acknowledgements

Computational (and/or data visualization) resources and services used in this work were provided by the HPC and Research Support Group, Queensland University of Technology, Brisbane, Australia. This project was supported in part by an Australian Research Council Linkage grant LP 130100110.

Author information

Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Ivan Himawan, Sridha Sridharan & Clinton Fookes

  2. Idiap Research Institute, Martigny, Switzerland

    Srikanth Madikeri & Petr Motlicek

  3. Logitech, Lausanne, Switzerland

    Milos Cernak

Authors
  1. Ivan Himawan
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  2. Srikanth Madikeri
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  3. Petr Motlicek
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  4. Milos Cernak
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  5. Sridha Sridharan
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  6. Clinton Fookes
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Correspondence to Ivan Himawan .

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Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, Southampton, UK

    Mark S. Nixon

  3. Universidad Autonoma de Madrid, Madrid, Spain

    Julian Fierrez

  4. EURECOM, Biot Sophia Antipolis, France

    Nicholas Evans

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Himawan, I., Madikeri, S., Motlicek, P., Cernak, M., Sridharan, S., Fookes, C. (2019). Voice Presentation Attack Detection Using Convolutional Neural Networks. In: Marcel, S., Nixon, M., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-92627-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-92627-8_17

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