mlsanityData to 14 Oct 2026

Robust face anti-spoofing framework with Convolutional Vision Transformer

arXiv 2307.12459 ↗First posted 24 Jul 2023cs.CVcited by 2

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Abstract (arXiv)

Owing to the advances in image processing technology and large-scale datasets, companies have implemented facial authentication processes, thereby stimulating increased focus on face anti-spoofing (FAS) against realistic presentation attacks. Recently, various attempts have been made to improve face recognition performance using both global and local learning on face images; however, to the best of our knowledge, this is the first study to investigate whether the robustness of FAS against domain shifts is improved by considering global information and local cues in face images captured using self-attention and convolutional layers. This study proposes a convolutional vision transformer-based framework that achieves robust performance for various unseen domain data. Our model resulted in 7.3%p and 12.9%p increases in FAS performance compared to models using only a convolutional neural network or vision transformer, respectively. It also shows the highest average rank in sub-protocols of cross-dataset setting over the other nine benchmark models for domain generalization.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 24 Jul 2023.

Builds on

23 of 32 references.

References · 23

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11 more
  1. 2022Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofingcited by 73
  2. 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 65
  3. 2021Self-Domain Adaptation for Face Anti-Spoofingcited by 49
  4. 2021Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofingcited by 49
  5. 2021Generalizable Representation Learning for Mixture Domain Face Anti-Spoofingcited by 46
  6. 2020On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofingcited by 44
  7. 2021Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltalescited by 39
  8. 2022Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofingcited by 38
  9. 2022ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detectioncited by 14
  10. 2018A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofingcited by 10
  11. 2023Liveness score-based regression neural networks for face anti-spoofingcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofingcited by 1
  2. 2026Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networkscited by 0