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Enhancing Face Anti-spoofing Systems Through Synthetic Image Generation

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Applied Informatics (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1874))

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Abstract

This study introduces a strategy for synthetic image generation aimed at enhancing the detection capability of facial authentication systems (FAS). By employing various digital manipulation techniques, new synthetic fake images were generated using existing datasets. Through experiments and result analysis, the impact of using these new fake samples on improving the detection accuracy of FAS systems was evaluated. The findings demonstrated the effectiveness of synthetic image generation in augmenting the diversity and complexity of the training data. Fine-tuning using the enhanced datasets significantly improved the detection accuracy across the evaluated FAS systems. Nonetheless, the degree of improvement varied among systems, indicating varying susceptibility to specific types of attacks.

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Notes

  1. 1.

    https://github.com/opencv/opencv/tree/master.

  2. 2.

    https://github.com/minivision-ai/Silent-Face-Anti-Spoofing/.

  3. 3.

    https://github.com/birdowl21/Face-Liveness-Detection-Anti-Spoofing-Web-App.

  4. 4.

    https://www.idiap.ch/en/dataset/replayattack.

  5. 5.

    https://github.com/ee09115/spoofing_detection.

  6. 6.

    https://www.iso.org/standard/79520.html.

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Authors and Affiliations

  1. Universidad de los Andes, Bogotá, Colombia

    César Vega & Ruben Manrique

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  1. César Vega
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  2. Ruben Manrique
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Correspondence to César Vega .

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Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Universidad Ecotec, Guayaquil, Ecuador

    Marcelo Leon

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Vega, C., Manrique, R. (2024). Enhancing Face Anti-spoofing Systems Through Synthetic Image Generation. In: Florez, H., Leon, M. (eds) Applied Informatics. ICAI 2023. Communications in Computer and Information Science, vol 1874. Springer, Cham. https://doi.org/10.1007/978-3-031-46813-1_2

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  • DOI: https://doi.org/10.1007/978-3-031-46813-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46812-4

  • Online ISBN: 978-3-031-46813-1

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