mlsanityData to 7 Oct 2026

Deep Tree Learning for Zero-shot Face Anti-Spoofing

arXiv 1904.02860 ↗First posted 5 Apr 2019cs.CVcited by 73

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Abstract (arXiv)

Face anti-spoofing is designed to keep face recognition systems from recognizing fake faces as the genuine users. While advanced face anti-spoofing methods are developed, new types of spoof attacks are also being created and becoming a threat to all existing systems. We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA only study 1-2 types of spoof attacks, such as print/replay attacks, which limits the insight of this problem. In this work, we expand the ZSFA problem to a wide range of 13 types of spoof attacks, including print attack, replay attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed to tackle the ZSFA. The tree is learned to partition the spoof samples into semantic sub-groups in an unsupervised fashion. When a data sample arrives, being know or unknown attacks, DTN routes it to the most similar spoof cluster, and make the binary decision. In addition, to enable the study of ZSFA, we introduce the first face anti-spoofing database that contains diverse types of spoof attacks. Experiments show that our proposed method achieves the state of the art on multiple testing protocols of ZSFA.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 26; 2022: 13; 2023: 19; 2024: 10; 2025: 4; 2026: 1.

73 papers.

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  1. 2022Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofingcited by 73
  2. 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 64
  3. 2021Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofingcited by 49
  4. 2021Dual-Cross Central Difference Network for Face Anti-Spoofingcited by 47
  5. 2023Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignmentcited by 47
  6. 2023FLIP: Cross-domain Face Anti-spoofing with Language Guidancecited by 45
  7. 2021Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detectioncited by 43
  8. 2021Cross Modal Focal Loss for RGBD Face Anti-Spoofingcited by 41
  9. 2021Biometrics: Trust, but Verifycited by 39
  10. 2021Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltalescited by 39
  11. 2022Multi-domain Learning for Updating Face Anti-spoofing Modelscited by 39
  12. 2024CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofingcited by 39
38 more
  1. 2022Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofingcited by 38
  2. 2023Tame a Wild Camera: In-the-Wild Monocular Camera Calibrationcited by 34
  3. 2021Detection and Continual Learning of Novel Face Presentation Attackscited by 33
  4. 2021Meta-Teacher For Face Anti-Spoofingcited by 33
  5. 2021Unified Detection of Digital and Physical Face Attackscited by 31
  6. 2021Learning Meta Pattern for Face Anti-Spoofingcited by 29
  7. 2022Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofingcited by 28
  8. 2022Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Eracited by 26
  9. 2022Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cuescited by 24
  10. 2021Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofingcited by 23
  11. 2023ChatGPT-Powered Hierarchical Comparisons for Image Classificationcited by 22
  12. 2021Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detectioncited by 20
  13. 2021Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learningcited by 20
  14. 2024Test-Time Domain Generalization for Face Anti-Spoofingcited by 20
  15. 2021Structure Destruction and Content Combination for Face Anti-Spoofingcited by 18
  16. 2022Digital and Physical Face Attacks: Reviewing and One Step Furthercited by 17
  17. 2023SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Datacited by 17
  18. 2024Unified Physical-Digital Face Attack Detectioncited by 17
  19. 2022One-Class Knowledge Distillation for Face Presentation Attack Detectioncited by 12
  20. 2023Surveillance Face Anti-spoofingcited by 12
  21. 2025Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistantscited by 12
  22. 2025LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognitioncited by 12
  23. 2023S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokenscited by 11
  24. 2021Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofingcited by 10
  25. 2021Consistency Regularization for Deep Face Anti-Spoofingcited by 10
  26. 2021Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting Systemcited by 9
  27. 2021Federated Generalized Face Presentation Attack Detectioncited by 9
  28. 2023Visual Prompt Flexible-Modal Face Anti-Spoofingcited by 9
  29. 2021On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detectioncited by 8
  30. 2022A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detectioncited by 7
  31. 2022VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and Adaptationcited by 7
  32. 2024DiffFAS: Face Anti-Spoofing via Generative Diffusion Modelscited by 7
  33. 2023Domain Generalization via Ensemble Stacking for Face Presentation Attack Detectioncited by 6
  34. 2021Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixingcited by 5
  35. 2021Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservationcited by 5
  36. 2023M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing Systemcited by 5
  37. 2025Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofingcited by 5
  38. 2025Harnessing Chain-of-Thought Reasoning in Multimodal Large Language Models for Face Anti-Spoofingcited by 5

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