Skip to main content

Multi-domain Learning for Updating Face Anti-spoofing Models

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

In this work, we study multi-domain learning for face anti-spoofing (MD-FAS), where a pre-trained FAS model needs to be updated to perform equally well on both source and target domains while only using target domain data for updating. We present a new model for MD-FAS, which addresses the forgetting issue when learning new domain data, while possessing a high level of adaptability. First, we devise a simple yet effective module, called spoof region estimator (SRE), to identify spoof traces in the spoof image. Such spoof traces reflect the source pre-trained model’s responses that help upgraded models combat catastrophic forgetting during updating. Unlike prior works that estimate spoof traces which generate multiple outputs or a low-resolution binary mask, SRE produces one single, detailed pixel-wise estimate in an unsupervised manner. Secondly, we propose a novel framework, named FAS-wrapper, which transfers knowledge from the pre-trained models and seamlessly integrates with different FAS models. Lastly, to help the community further advance MD-FAS, we construct a new benchmark based on SIW, SIW-Mv2 and Oulu-NPU, and introduce four distinct protocols for evaluation, where source and target domains are different in terms of spoof type, age, ethnicity, and illumination. Our proposed method achieves superior performance on the MD-FAS benchmark than previous methods. Our code is available at https://github.com/CHELSEA234/Multi-domain-learning-FAS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    We release SiW-Mv2 on CVLab website.

References

  1. international organization for standardization. Iso/iec jtc 1/sc 37 biometrics: Information technology biometric presentation attack detection part 1: Framework. https://www.iso.org/obp/ui/iso. Accessed 3 Mar 2022

  2. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: OSDI (2016)

    Google Scholar 

  3. AbdAlmageed, W., et al.: Assessment of facial morphologic features in patients with congenital adrenal hyperplasia using deep learning. JAMA Netw. Open 3 (2020)

    Google Scholar 

  4. Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: learning what (not) to forget. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 144–161. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_9

    Chapter  Google Scholar 

  5. Asnani, V., Yin, X., Hassner, T., Liu, S., Liu, X.: Proactive image manipulation detection. In: CVPR (2022)

    Google Scholar 

  6. Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNS. In: IJCB (2017)

    Google Scholar 

  7. Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: IEEE International Conference on Automatic Face and Gesture Recognition (2017)

    Google Scholar 

  8. Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with a-gem. ICLR (2019)

    Google Scholar 

  9. Chung, I., Park, S., Kim, J., Kwak, N.: Feature-map-level online adversarial knowledge distillation. In: ICML (2020)

    Google Scholar 

  10. Dang*, H., Liu*, F., Stehouwer*, J., Liu, X., Jain, A.: On the detection of digital face manipulation. In: CVPR (2020)

    Google Scholar 

  11. Delange, M., et al.: A continual learning survey: Defying forgetting in classification tasks. In: TPAMI (2021)

    Google Scholar 

  12. Dhar, P., Singh, R.V., Peng, K.C., Wu, Z., Chellappa, R.: Learning without memorizing. In: CVPR (2019)

    Google Scholar 

  13. Fernando, C., et al.: PathNet: evolution channels gradient descent in super neural networks. arXiv preprint arXiv:1701.08734 (2017)

  14. Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 2096–2030 (2016)

    Google Scholar 

  15. Guo, X., Choi, J.: Human motion prediction via learning local structure representations and temporal dependencies. In: AAAI (2019)

    Google Scholar 

  16. Guo, X., Mirzaalian, H., Sabir, E., Jaiswal, A., Abd-Almageed, W.: Cord19sts: Covid-19 semantic textual similarity dataset. arXiv preprint arXiv:2007.02461 (2020)

  17. Guo, Y., Li, Y., Wang, L., Rosing, T.: Depthwise convolution is all you need for learning multiple visual domains. In: AAAI (2019)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  19. Hsu, I., et al.: Discourse-level relation extraction via graph pooling. arXiv preprint arXiv:2101.00124 (2021)

  20. Jia, Y., Zhang, J., Shan, S., Chen, X.: Single-side domain generalization for face anti-spoofing. In: CVPR (2020)

    Google Scholar 

  21. Jourabloo, A., Liu, Y., Liu, X.: Face de-spoofing: Anti-spoofing via noise modeling. In: ECCV (2018)

    Google Scholar 

  22. Jung, H., Ju, J., Jung, M., Kim, J.: Less-forgetting learning in deep neural networks. arXiv preprint arXiv:1607.00122 (2016)

  23. Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 158–171. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_12

    Chapter  Google Scholar 

  24. Kim, J.Y., Choi, D.W.: Split-and-bridge: Adaptable class incremental learning within a single neural network. In: AAAI (2021)

    Google Scholar 

  25. Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. In: Proceedings of the National Academy of Sciences (2017)

    Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeuriPS (2012)

    Google Scholar 

  27. Kundu, J.N., Venkatesh, R.M., Venkat, N., Revanur, A., Babu, R.V.: Class-incremental domain adaptation. In: ECCV (2020)

    Google Scholar 

  28. Lee, S.W., Kim, J.H., Jun, J., Ha, J.W., Zhang, B.T.: Overcoming catastrophic forgetting by incremental moment matching. In: NeurIps (2017)

    Google Scholar 

  29. Li, Z., Hoiem, D.: Learning without forgetting. In: TPAMI (2017)

    Google Scholar 

  30. Liu, A., Tan, Z., Wan, J., Escalera, S., Guo, G., Li, S.Z.: URF CeFA: a benchmark for multi-modal cross-ethnicity face anti-spoofing. In: WACV (2021)

    Google Scholar 

  31. Liu, H., Cao, Z., Long, M., Wang, J., Yang, Q.: Separate to adapt: open set domain adaptation via progressive separation. In: CVPR (2019)

    Google Scholar 

  32. Liu, S., Yang, B., Yuen, P.C., Zhao, G.: A 3d mask face anti-spoofing database with real world variations. In: CVPR Workshop (2016)

    Google Scholar 

  33. Liu, X., Liu, Y., Chen, J., Liu, X.: PSSC-Net: progressive spatio-channel correlation network for image manipulation detection and localization. In: T-CSVT (2022)

    Google Scholar 

  34. Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In: CVPR (2018)

    Google Scholar 

  35. Liu, Y., Liu, X.: Physics-guided spoof trace disentanglement for generic face anti-spoofing. In: TPAMI (2022)

    Google Scholar 

  36. Liu, Y., Stehouwer, J., Jourabloo, A., Liu, X.: Deep tree learning for zero-shot face anti-spoofing. In: CVPR (2019)

    Google Scholar 

  37. Liu, Y., Stehouwer, J., Liu, X.: On disentangling spoof trace for generic face anti-spoofing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 406–422. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58523-5_24

    Chapter  Google Scholar 

  38. Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: ICML (2015)

    Google Scholar 

  39. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (1967)

    Google Scholar 

  40. Mallya, A., Lazebnik, S.: Packnet: Adding multiple tasks to a single network by iterative pruning. In: CVPR (2018)

    Google Scholar 

  41. Mancini, M., Ricci, E., Caputo, B., Bulò, S.R.: Adding new tasks to a single network with weight transformations using binary masks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11130, pp. 180–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11012-3_14

    Chapter  Google Scholar 

  42. Qin, Y., et al.: Learning meta model for zero-and few-shot face anti-spoofing. In: AAAI (2020)

    Google Scholar 

  43. Quan, R., Wu, Y., Yu, X., Yang, Y.: Progressive transfer learning for face anti-spoofing. In: TIP (2021)

    Google Scholar 

  44. Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: NeurIPS (2017)

    Google Scholar 

  45. Rebuffi, S.A., Bilen, H., Vedaldi, A.: Efficient parametrization of multi-domain deep neural networks. In: CVPR (2018)

    Google Scholar 

  46. Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: ICARL: incremental classifier and representation learning. In: CVPR (2017)

    Google Scholar 

  47. Rostami, M., Spinoulas, L., Hussein, M., Mathai, J., Abd-Almageed, W.: Detection and continual learning of novel face presentation attacks. In: ICCV (2021)

    Google Scholar 

  48. Rothe, R., Timofte, R., Van Gool, L.: DEX: deep expectation of apparent age from a single image. In: ICCV Workshops (2015)D

    Google Scholar 

  49. Rusu, A.A., et al.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016)

  50. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting Visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16

    Chapter  Google Scholar 

  51. Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: ECCV (2018)

    Google Scholar 

  52. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision 128(2), 336–359 (2019). https://doi.org/10.1007/s11263-019-01228-7

    Article  Google Scholar 

  53. Shao, R., Lan, X., Li, J., Yuen, P.C.: Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In: CVPR (2019)

    Google Scholar 

  54. Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. NeurIPS (2017)

    Google Scholar 

  55. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  56. Stehouwer, J., Jourabloo, A., Liu, Y., Liu, X.: Noise modeling, synthesis and classification for generic object anti-spoofing. In: CVPR (2020)

    Google Scholar 

  57. Thorndike, R.L.: Who belongs in the family? Psychometrika 18, 267–276 (1953)

    Google Scholar 

  58. Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR (2011)

    Google Scholar 

  59. Tu, X., Ma, Z., Zhao, J., Du, G., Xie, M., Feng, J.: Learning generalizable and identity-discriminative representations for face anti-spoofing. In: TIST (2020)

    Google Scholar 

  60. Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: ICCV (2019)

    Google Scholar 

  61. Yang, B., Zhang, J., Yin, Z., Shao, J.: Few-shot domain expansion for face anti-spoofing. arXiv preprint arXiv:2106.14162 (2021)

  62. Yoo, J., Uh, Y., Chun, S., Kang, B., Ha, J.W.: Photorealistic style transfer via wavelet transforms. In: ICCV (2019)

    Google Scholar 

  63. Yu, Z., Li, X., Niu, X., Shi, J., Zhao, G.: Face anti-spoofing with human material perception. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 557–575. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58571-6_33

    Chapter  Google Scholar 

  64. Yu, Z., Li, X., Shi, J., Xia, Z., Zhao, G.: Revisiting pixel-wise supervision for face anti-spoofing. Behavior, and Identity Science, IEEE Trans. Biomet. 3, 285–295 (2021)

    Google Scholar 

  65. Yu, Z., et al.: Searching central difference convolutional networks for face anti-spoofing. In: CVPR (2020)

    Google Scholar 

  66. Zhang, J., et al.: Class-incremental learning via deep model consolidation. In: WACV (2020)

    Google Scholar 

  67. Zhang, S., et al.: CASIA-SURF: a large-scale multi-modal benchmark for face anti-spoofing. Behavior, and Identity Science, IEEE Trans. Biomet. 2, 182–193 (2020)

    Google Scholar 

  68. Zhang, Y., et al.: CelebA-Spoof: large-scale face anti-spoofing dataset with rich annotations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 70–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_5

    Chapter  Google Scholar 

  69. Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. In: CVFPR (2021)

    Google Scholar 

  70. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)

    Google Scholar 

  71. Zhou, H., Hadap, S., Sunkavalli, K., Jacobs, D.W.: Deep single-image portrait relighting. In: ICCV (2019)

    Google Scholar 

Download references

Acknowledgement

This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R &D Contract No. 2017-17020200004. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiao Guo .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1393 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guo, X., Liu, Y., Jain, A., Liu, X. (2022). Multi-domain Learning for Updating Face Anti-spoofing Models. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13673. Springer, Cham. https://doi.org/10.1007/978-3-031-19778-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19778-9_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19777-2

  • Online ISBN: 978-3-031-19778-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics