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Introducing Nylon Face Mask Attacks: A Dataset for Evaluating Generalised Face Presentation Attack Detection

arXiv 2511.08114 ↗First posted 11 Nov 2025cs.CV cs.ETcited by 0

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Abstract (arXiv)

Face recognition systems are increasingly deployed across a wide range of applications, including smartphone authentication, access control, and border security. However, these systems remain vulnerable to presentation attacks (PAs), which can significantly compromise their reliability. In this work, we introduce a new dataset focused on a novel and realistic presentation attack instrument called Nylon Face Masks (NFMs), designed to simulate advanced 3D spoofing scenarios. NFMs are particularly concerning due to their elastic structure and photorealistic appearance, which enable them to closely mimic the victim's facial geometry when worn by an attacker. To reflect real-world smartphone-based usage conditions, we collected the dataset using an iPhone 11 Pro, capturing 3,760 bona fide samples from 100 subjects and 51,281 NFM attack samples across four distinct presentation scenarios involving both humans and mannequins. We benchmark the dataset using five state-of-the-art PAD methods to evaluate their robustness under unseen attack conditions. The results demonstrate significant performance variability across methods, highlighting the challenges posed by NFMs and underscoring the importance of developing PAD techniques that generalise effectively to emerging spoofing threats.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 11 Nov 2025.

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20 of 48 references.

References · 20

  1. 2020CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotationscited by 109
  2. 2019Regularized Fine-grained Meta Face Anti-spoofingcited by 86
  3. 2015face anti-spoofing based on color texture analysiscited by 82
  4. 2014Learn Convolutional Neural Network for Face Anti-Spoofingcited by 80
  5. 2022PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognitioncited by 67
  6. 2020Face Morphing Attack Generation & Detection: A Comprehensive Surveycited by 64
  7. 2021Generalizable Representation Learning for Mixture Domain Face Anti-Spoofingcited by 46
  8. 2023FLIP: Cross-domain Face Anti-spoofing with Language Guidancecited by 45
  9. 2020On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofingcited by 44
  10. 2024CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofingcited by 39
  11. 2020Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networkscited by 36
  12. 2024Gradient Alignment for Cross-Domain Face Anti-Spoofingcited by 27
8 more
  1. 2020Anomaly Detection-Based Unknown Face Presentation Attack Detectioncited by 20
  2. 2021Learnable Multi-level Frequency Decomposition and Hierarchical Attention Mechanism for Generalized Face Presentation Attack Detectioncited by 18
  3. 2025FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detectioncited by 14
  4. 2022One-Class Knowledge Distillation for Face Presentation Attack Detectioncited by 13
  5. 2023Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Lesscited by 12
  6. 2023Face Presentation Attack Detection by Excavating Causal Clues and Adapting Embedding Statisticscited by 7
  7. 2023Domain-Generalized Face Anti-Spoofing with Unknown Attackscited by 4
  8. 2021Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Surveycited by 2

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