mlsanityData to 7 Oct 2026

Flexible-Modal Face Anti-Spoofing: A Benchmark

arXiv 2202.08192 ↗First posted 16 Feb 2022cs.CVcited by 20

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Abstract (arXiv)

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Benefitted from the maturing camera sensors, single-modal (RGB) and multi-modal (e.g., RGB+Depth) FAS has been applied in various scenarios with different configurations of sensors/modalities. Existing single- and multi-modal FAS methods usually separately train and deploy models for each possible modality scenario, which might be redundant and inefficient. Can we train a unified model, and flexibly deploy it under various modality scenarios? In this paper, we establish the first flexible-modal FAS benchmark with the principle `train one for all'. To be specific, with trained multi-modal (RGB+Depth+IR) FAS models, both intra- and cross-dataset testings are conducted on four flexible-modal sub-protocols (RGB, RGB+Depth, RGB+IR, and RGB+Depth+IR). We also investigate prevalent deep models and feature fusion strategies for flexible-modal FAS. We hope this new benchmark will facilitate the future research of the multi-modal FAS. The protocols and codes are available at https://github.com/ZitongYu/Flex-Modal-FAS.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 16 Feb 2022.

Builds on

27 of 43 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
  5. 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
  6. 2020NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofingcited by 104
  7. 2015face anti-spoofing based on color texture analysiscited by 82
  8. 2019Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Networkcited by 78
  9. 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
  10. 2020CASIA-SURF CeFA: A Benchmark for Multi-modal Cross-ethnicity Face Anti-spoofingcited by 66
  11. 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 64
  12. 2020Face Anti-Spoofing with Human Material Perceptioncited by 62
15 more
  1. 2018A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofingcited by 51
  2. 2021Dual-Cross Central Difference Network for Face Anti-Spoofingcited by 47
  3. 2020Revisiting Pixel-Wise Supervision for Face Anti-Spoofingcited by 45
  4. 2019Learning Meta Model for Zero- and Few-shot Face Anti-spoofingcited by 44
  5. 2020On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofingcited by 44
  6. 2021Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detectioncited by 43
  7. 2021Cross Modal Focal Loss for RGBD Face Anti-Spoofingcited by 41
  8. 2021TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detectioncited by 41
  9. 2020Multi-Modal Face Anti-Spoofing Based on Central Difference Networkscited by 36
  10. 2021Meta-Teacher For Face Anti-Spoofingcited by 33
  11. 2021Learning Meta Pattern for Face Anti-Spoofingcited by 29
  12. 2020Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Reviewcited by 25
  13. 2020DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofingcited by 25
  14. 20213D High-Fidelity Mask Face Presentation Attack Detection Challengecited by 18
  15. 2021Review of Face Presentation Attack Detection Competitionscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 5; 2025: 6; 2026: 1.

20 papers.

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  1. 2024CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofingcited by 39
  2. 2022Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cuescited by 24
  3. 2023FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofingcited by 22
  4. 2023Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofingcited by 19
  5. 2023Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Lesscited by 12
  6. 2023Visual Prompt Flexible-Modal Face Anti-Spoofingcited by 9
  7. 2024Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Cluescited by 8
  8. 2025Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineeringcited by 7
  9. 2025Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detectioncited by 5
  10. 2025DADM: Dual Alignment of Domain and Modality for Face Anti-spoofingcited by 4
  11. 2024Event-based Video Frame Interpolation with Edge Guided Motion Refinementcited by 3
  12. 2025Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detectioncited by 2
8 more
  1. 2023Flexible-modal Deception Detection with Audio-Visual Adaptercited by 1
  2. 2023IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Imagescited by 1
  3. 2024Zero-failure testing of binary classifierscited by 1
  4. 2023Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attackscited by 0
  5. 2024FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Modelcited by 0
  6. 2025Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitionscited by 0
  7. 2025Robust Multi-Modal Face Anti-Spoofing with Domain Adaptation: Tackling Missing Modalities, Noisy Pseudo-Labels, and Model Degradationcited by 0
  8. 2026MMDG-Bench: A Benchmark for Multimodal Domain Generalizationcited by 0