mlsanityData to 7 Oct 2026

Unified Detection of Digital and Physical Face Attacks

arXiv 2104.02156 ↗First posted 5 Apr 2021cs.CVcited by 31

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Abstract (arXiv)

State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories. Poor generalization can be attributed to learning incoherent attacks jointly. To overcome this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories. Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately. Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94.73% @ 0.2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories. Proposed method can detect an attack within 3 milliseconds on a Nvidia 2080Ti. UniFAD can also identify the attack types and categories with 75.81% and 97.37% accuracies, respectively.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 42 of its 79 references that are in the map. First posted on arXiv 5 Apr 2021.

Builds on

42 of 79 references.

References · 42

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  6. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  7. 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
  8. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
  9. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
  10. 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
  11. 2019Face X-ray for More General Face Forgery Detectioncited by 469
  12. 2014Learning Face Representation from Scratchcited by 451
30 more
  1. 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 374
  2. 2018Exposing Deep Fakes Using Inconsistent Head Posescited by 294
  3. 2019On the Detection of Digital Face Manipulationcited by 259
  4. 2018DeepFakes: a New Threat to Face Recognition? Assessment and Detectioncited by 208
  5. 2020A Self-supervised Approach for Adversarial Robustnesscited by 180
  6. 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
  7. 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
  8. 2021PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localizationcited by 133
  9. 2018Two-Stream Neural Networks for Tampered Face Detectioncited by 125
  10. 2019STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editingcited by 119
  11. 2015Multi-task Sequence to Sequence Learningcited by 94
  12. 2019SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editingcited by 93
  13. 2018Unsupervised Learning via Meta-Learningcited by 81
  14. 2014Learn Convolutional Neural Network for Face Anti-Spoofingcited by 80
  15. 2019Deep Tree Learning for Zero-shot Face Anti-Spoofingcited by 73
  16. 2020On Disentangling Spoof Trace for Generic Face Anti-Spoofingcited by 62
  17. 2018Face De-Spoofing: Anti-Spoofing via Noise Modelingcited by 60
  18. 2019AdvFaces: Adversarial Face Synthesiscited by 59
  19. 2017Adversarial and Clean Data Are Not Twinscited by 52
  20. 2020Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networkscited by 36
  21. 2017Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognitioncited by 24
  22. 2018Fast Geometrically-Perturbed Adversarial Facescited by 22
  23. 2020Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approachcited by 18
  24. 2019Detection of Face Recognition Adversarial Attackscited by 15
  25. 2020FaceGuard: A Self-Supervised Defense Against Adversarial Face Imagescited by 15
  26. 2020Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofingcited by 14
  27. 2020Learning Generalized Spoof Cues for Face Anti-spoofingcited by 14
  28. 2018Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Backcited by 9
  29. 2019Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Featurescited by 2
  30. 2020MixNet for Generalized Face Presentation Attack Detectioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 6; 2024: 8; 2025: 7; 2026: 3.

31 papers.

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  1. 2023Hierarchical Fine-Grained Image Forgery Detection and Localizationcited by 134
  2. 2021Deep Learning for Face Anti-Spoofing: A Surveycited by 64
  3. 2023Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignmentcited by 47
  4. 2021Biometrics: Trust, but Verifycited by 39
  5. 2025Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detectorcited by 38
  6. 2022Proactive Image Manipulation Detectioncited by 35
  7. 2022GAN-generated Faces Detection: A Survey and New Perspectivescited by 29
  8. 2023MaLP: Manipulation Localization Using a Proactive Schemecited by 26
  9. 2022Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cuescited by 24
  10. 2023ChatGPT-Powered Hierarchical Comparisons for Image Classificationcited by 22
  11. 2024FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learningcited by 21
  12. 2022Digital and Physical Face Attacks: Reviewing and One Step Furthercited by 17
19 more
  1. 2024Unified Physical-Digital Face Attack Detectioncited by 17
  2. 2024Language-guided Hierarchical Fine-grained Image Forgery Detection and Localizationcited by 16
  3. 2021Differential Anomaly Detection for Facial Imagescited by 11
  4. 2023Adversarial Examples in the Physical World: A Surveycited by 10
  5. 2024La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detectioncited by 8
  6. 2025Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detectioncited by 5
  7. 2025Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuningcited by 5
  8. 2025Harnessing Chain-of-Thought Reasoning in Multimodal Large Language Models for Face Anti-Spoofingcited by 5
  9. 2024A Survey on Physical Adversarial Attacks against Face Recognition Systemscited by 4
  10. 2023AdvFAS: A robust face anti-spoofing framework against adversarial examplescited by 3
  11. 2024Unified Physical-Digital Attack Detection Challengecited by 3
  12. 2025FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detectioncited by 3
  13. 2024Principles of Designing Robust Remote Face Anti-Spoofing Systemscited by 2
  14. 2024FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Modelcited by 0
  15. 2025Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detectioncited by 0
  16. 2025Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detectioncited by 0
  17. 2026Face Presentation Attack Detection via Content-Adaptive Spatial Operatorscited by 0
  18. 2026Unified Face Attack Detection via Fine-Grained Semantic Guidancecited by 0
  19. 2026MS-MFAD : Multimodal large language models for Face Anti-spoofing Detectioncited by 0