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Open-Set Single-Domain Generalization for Robust Face Anti-Spoofing

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Abstract

Face anti-spoofing is a critical component of face recognition technology. However, it suffers from poor generalizability for cross-scenario target domains due to the simultaneous presence of unseen domains and unknown attack types. In this paper, we first propose a challenging but practical problem for face anti-spoofing, open-set single-domain generalization-based face anti-spoofing, aiming to learn face anti-spoofing models that generalize well to unseen target domains with known and unknown attack types based on a single source domain. To address this problem, we propose a novel unknown-aware causal generalized representation learning framework. Specifically, the proposed network consists of two modules: (1) causality-inspired intervention domain augmentation, which generates out-of-distribution images to eliminate spurious correlations between spoof-irrelevant variant factors and category labels for generalized causal feature learning; and (2) unknown-aware probability calibration, which performs known and unknown attack detection based on the original and generated images to further improve the generalizability for unknown attack types. The results of extensive qualitative and quantitative experiments demonstrate that the proposed method learns well-generalized features for both domain shift and unknown attack types based on a single source domain. Our method achieves state-of-the-art cross-scenario generalizability for both live faces and known attack types and unknown attack types.

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Data Availability

The data that support the findings of this study are available from the authors upon reasonable request.

Notes

  1. https://www.idiap.ch/en/dataset/hq-wmca.

  2. https://github.com/wgqtmac/DR-UDA.

  3. https://github.com/taylover-pei/USDAN-PR.

  4. https://github.com/doantienthongbku/Implementation-patchnet.

  5. https://github.com/lileicv/PDEN.

  6. https://github.com/BUserName/Learning_to_diversify.

References

  • Abduh, L., & Ivrissimtzis, I. (2020). Use of in-the-wild images for anomaly detection in face anti-spoofing. arXiv:2006.10626

  • Arashloo, S. R. (2020). Unseen face presentation attack detection using sparse multiple kernel fisher null-space. IEEE Transactions on Circuits and Systems for Video Technology, 31(10), 4084–4095.

    Article  Google Scholar 

  • Arashloo, S. R. (2023). Unknown face presentation attack detection via localized learning of multiple kernels. IEEE Transactions on Information Forensics and Security, 18, 1421–1432.

    Article  Google Scholar 

  • Boulkenafet, Z., Komulainen, J., & Hadid, A. (2016). Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security, 11(8), 1818–1830.

    Article  Google Scholar 

  • Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., & Hadid, A. (2017). Oulu-npu: A mobile face presentation attack database with real-world variations. In Proceedings of international conference on automatic face and gesture recognition (pp. 612–618).

  • Cai, R., Li, Z., Wan, R., Li, H., Hu, Y., & Kot, A. C. (2022). Learning meta pattern for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 17, 1201–1213.

    Article  Google Scholar 

  • Chingovska, I., Anjos, A., & Marcel, S. (2012). On the effectiveness of local binary patterns in face anti-spoofing. In Proceedings of international conference of the biometrics special interes group (pp. 1–7).

  • Du, Z., Li, J., Zuo L., Zhu, L., & Lu, K. (2022). Energy-based domain generalization for face anti-spoofing. In Proceedings of international conference on multimedia (pp. 1749–1757).

  • El-Din, Y. S., Moustafa, M. N., & Mahdi, H. (2021). Adversarial unsupervised domain adaptation guided with deep clustering for face presentation attack detection. arXiv:2102.06864

  • Fang, H., Liu, A., Wan, J., Escalera, S., Zhao, C., Zhang, X., Li, S. Z., & Lei, Z. (2023). Surveillance face anti-spoofing. arXiv:2301.00975.

  • Feng, H., Hong, Z., Yue, H., Chen, Y., Wang, K., Han, J., Liu, J., & Ding, E. (2020). Learning generalized spoof cues for face anti-spoofing. arXiv:2005.03922.

  • Guo, X., Liu, Y., Jain, A., & Liu, X. (2022). Multi-domain learning for updating face anti-spoofing models. In Proceedings of European conference on computer vision (pp. 230–249).

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 770–778).

  • Heusch, G., George, A., Geissbühler, D., Mostaani, Z., & Marcel, S. (2020). Deep models and shortwave infrared information to detect face presentation attacks. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2(4), 399–409.

    Article  Google Scholar 

  • Huang, H. P., Sun, D., Liu, Y., Chu, W. S., Xiao, T., Yuan, J., Adam, H., & Yang, M. H. (2022). Adaptive transformers for robust few-shot cross-domain face anti-spoofing. In Proceedings of European conference on computer vision (pp. 37–54).

  • Jia, Y., Zhang, J., Shan, S., & Chen, X. (2020). Single-side domain generalization for face anti-spoofing. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 8484–8493).

  • Jia, Y., Zhang, J., & Shan, S. (2021). Dual-branch meta-learning network with distribution alignment for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 17, 138–151.

    Article  Google Scholar 

  • Jia, Y., Zhang, J., Shan, S., & Chen, X. (2021). Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing. Pattern Recognition, 115, 107888.

    Article  Google Scholar 

  • Jiang, F., Li, Q., Liu, P., Zhou, X. D., & Sun, Z. (2023). Adversarial learning domain-invariant conditional features for robust face anti-spoofing. International Journal of Computer Vision, 131, 1680–1703.

    Article  Google Scholar 

  • Kim, T., & Kim, Y. (2021). Suppressing spoof-irrelevant factors for domain-agnostic face anti-spoofing. IEEE Access, 9, 86966–86974.

    Article  Google Scholar 

  • Li, H., He, P., Wang, S., Rocha, A., Jiang, X., & Kot, A. C. (2018). Learning generalized deep feature representation for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 13(10), 2639–2652.

    Article  Google Scholar 

  • Li, H., Li, W., Cao, H., Wang, S., Huang, F., & Kot, A. C. (2018). Unsupervised domain adaptation for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 13(7), 1794–1809.

    Article  Google Scholar 

  • Li, H., Wang, S., He, P., & Rocha, A. (2020). Face anti-spoofing with deep neural network distillation. Journal of Selected Topics in Signal Processing, 14(5), 933–946.

    Article  Google Scholar 

  • Li, L., Gao, K., Cao, J., Huang, Z., Weng, Y., Mi, X., Yu, Z., Li, X., & Xia, B. (2021). Progressive domain expansion network for single domain generalization. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 224–233)

  • Li, Z., Li, H., Lam, K. Y., & Kot, A. C. (2020b). Unseen face presentation attack detection with hypersphere loss. In Proceedings of international conference on acoustics, speech and signal processing (pp. pp 2852–2856).

  • Liao, C. H., Chen, W. C., Liu, H. T., Yeh, Y. R., Hu, M. C., & Chen, C. S. (2023). Domain invariant vision transformer learning for face anti-spoofing. In Proceedings of winter conference on applications of computer vision (pp. 6098–6107).

  • Liu, A., Tan, Z., Wan, J., Escalera, S., Guo, G., & Li, S.Z. (2021). Casia-surf cefa: A benchmark for multi-modal cross-ethnicity face anti-spoofing. In Proceedings of IEEE winter conference on applications of computer vision (pp. 1179–1187).

  • Liu, A., Zhao, C., Yu, Z., Wan, J., Su, A., Liu, X., Tan, Z., Escalera, S., Xing, J., Liang, Y., et al. (2022). Contrastive context-aware learning for 3d high-fidelity mask face presentation attack detection. IEEE Transactions on Information Forensics and Security, 17, 2497–2507.

    Article  Google Scholar 

  • Liu, Y., Stehouwer, J., Jourabloo, A., & Liu, X. (2019). Deep tree learning for zero-shot face anti-spoofing. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 4680–4689)

  • Liu, Y., Stehouwer, J., & Liu, X. (2020). On disentangling spoof trace for generic face anti-spoofing. In Proceedings of European Conference on Computer Vision (pp. 406–422).

  • Liu, Y., Chen, Y., Dai, W., Gou, M., Huang, C.T., & Xiong, H. (2022b). Source-free domain adaptation with contrastive domain alignment and self-supervised exploration for face anti-spoofing. In Proceedings of European conference on computer vision (pp. 511–528).

  • Lvd, Maaten, & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(11), 2579–2605.

    Google Scholar 

  • Määttä, J., Hadid, A., & Pietikäinen, M. (2011). Face spoofing detection from single images using micro-texture analysis. In Proceedings of International Joint Conference on Biometrics (pp. 1–7).

  • Mao, C., Cha, A., Gupta, A., Wang, H., Yang, J., & Vondrick, C. (2021). Generative interventions for causal learning. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 3947–3956).

  • Mohammadi, A., Bhattacharjee, S., & Marcel, S. (2020a). Domain adaptation for generalization of face presentation attack detection in mobile settings with minimal information. In Proceedings of international conference on acoustics, speech and signal processing (pp. 1001–1005).

  • Mohammadi, A., Bhattacharjee, S., & Marcel, S. (2020b). Improving cross-dataset performance of face presentation attack detection systems using face recognition datasets. In Proceedings of international conference on acoustics, speech and signal processing (pp. 2947–2951).

  • Muhammad, U., Beddiar, D. R., Oussalah, M. (2023). Domain generalization via ensemble stacking for face presentation attack detection. arXiv:2301.02145

  • Nguyen, S.M., Tran, L.D., Le, D.V., & Masayuki, A. (2022). Self-attention generative distribution adversarial network for few-and zero-shot face anti-spoofing. In Proceedings of International Joint Conference on Biometrics (pp. 1–9).

  • Nikisins, O., Mohammadi, A., Anjos, A., & Marcel, S. (2018). On effectiveness of anomaly detection approaches against unseen presentation attacks in face anti-spoofing. In Proceedings of international conference on biometrics (pp. 75–81).

  • Ouyang, C., Chen, C., Li, S., Li, Z., Qin, C., Bai, W., & Rueckert, D. (2022). Causality-inspired single-source domain generalization for medical image segmentation. IEEE Transactions on Medical Imaging, 42(4), 1095–1106.

    Article  Google Scholar 

  • Pérez-Cabo, D., Jiménez-Cabello, D., Costa-Pazo, A., & López-Sastre, R. J. (2019). Deep anomaly detection for generalized face anti-spoofing. In Proceedings of IEEE conference on computer vision and pattern recognition workshops (pp. 1–8).

  • Qin, Y., Zhao, C., Zhu, X., Wang, Z., Yu, Z., Fu, T., Zhou, F., Shi, J., & Lei, Z. (2020). Learning meta model for zero-and few-shot face anti-spoofing. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11916–11923.

  • Quan, R., Wu, Y., Yu, X., & Yang, Y. (2021). Progressive transfer learning for face anti-spoofing. IEEE Transactions on Image Processing, 30, 3946–3955.

    Article  Google Scholar 

  • Rojas-Carulla, M., Schölkopf, B., Turner, R., & Peters, J. (2018). Invariant models for causal transfer learning. Journal of Machine Learning Research, 19(1), 1309–1342.

    MathSciNet  Google Scholar 

  • Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of IEEE international conference on computer vision (pp. 618–626).

  • Shao, R., Lan, X., Li, J., & Yuen, P. C. (2019). Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 10023–10031).

  • Shao, R., Lan, X., & Yuen, P. C. (2020). Regularized fine-grained meta face anti-spoofing. In Proceedings of the AAAI conference on artificial intelligence (vol. 34, pp. 11974–11981).

  • Sheth, P., Moraffah, R., Candan, K. S., Raglin, A., & Liu, H. (2022). Domain generalization a causal perspective. arXiv:2209.15177

  • Sun, Y., Liu, Y., Liu, X., Li, Y., & Chu, W. S. (2023). Rethinking domain generalization for face anti-spoofing: Separability and alignment. arXiv:2303.13662

  • Tu, X., Zhang, H., Xie, M., Luo, Y., Zhang, Y., & Ma, Z. (2019). Deep transfer across domains for face antispoofing. Journal of Electronic Imaging, 28(4), 043001.

    Article  Google Scholar 

  • Tu, X., Ma, Z., Zhao, J., Du, G., Xie, M., & Feng, J. (2020). Learning generalizable and identity-discriminative representations for face anti-spoofing. ACM Transactions on Intelligent Systems and Technology, 11(5), 1–19.

    Article  Google Scholar 

  • Volpi, R., Namkoong, H., Sener, O., Duchi, J.C., & Murino, V., Savarese, S. (2018). Generalizing to unseen domains via adversarial data augmentation. In Advances in neural information processing systems (pp. 5339–5349)

  • Wang, C. Y., Lu, Y. D., Yang, S. T., & Lai, S. H. (2022a). Patchnet: A simple face anti-spoofing framework via fine-grained patch recognition. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 20281–20290).

  • Wang, G., Han, H., Shan, S., & Chen, X. (2019). Improving cross-database face presentation attack detection via adversarial domain adaptation. In Proceedings of international conference on biometrics (pp. 1–8).

  • Wang, G., Han, H., Shan, S., & Chen, X. (2020). Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection. IEEE Transactions on Information Forensics and Security, 16, 56–69.

    Article  Google Scholar 

  • Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2023). Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering, 35(08), 8052–8072.

  • Wang, M., & Deng, W. (2018). Deep visual domain adaptation: A survey. Neurocomputing, 312, 135–153.

    Article  Google Scholar 

  • Wang, Z., Luo, Y., Qiu, R., Huang, Z., & Baktashmotlagh, M. (2021). Learning to diversify for single domain generalization. In Proceedings of IEEE international conference on computer vision (pp. 834–843).

  • Wang, Z., Wang, Z., Yu, Z., Deng, W., Li, J., Gao, T., & Wang, Z. (2022b). Domain generalization via shuffled style assembly for face anti-spoofing. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 4123–4133).

  • Wen, D., Han, H., & Jain, A. K. (2015). Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, 10(4), 746–761.

    Article  Google Scholar 

  • Yang, J., Lei, Z., & Li, S. Z. (2014). Learn convolutional neural network for face anti-spoofing. arXiv:1408.5601

  • Yu, Z., Qin, Y., Li, X., Zhao, C., Lei, Z., & Zhao, G. (2023). Deep learning for face anti-spoofing: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5609–5631.

    Google Scholar 

  • Yue, H., Wang, K., Zhang, G., Feng, H., Han, J., Ding, E., & Wang, J. (2022). Cyclically disentangled feature translation for face anti-spoofing. arXiv:2212.03651

  • Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499–1503.

    Article  Google Scholar 

  • Zhang, S., Liu, A., Wan, J., Liang, Y., Guo, G., Escalera, S., Escalante, H. J., & Li, S. Z. (2020). Casia-surf: A large-scale multi-modal benchmark for face anti-spoofing. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2(2), 182–193.

    Article  Google Scholar 

  • Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., & Li, S. Z. (2012). A face antispoofing database with diverse attacks. In Proceedings of International Conference on Biometrics (pp. 26–31).

  • Zheng, T. (2023). Mcae: Masked contrastive autoencoder for face anti-spoofing. arXiv:2302.08674

  • Zhou, Q., Zhang, K. Y., Yao, T., Yi, R., Ding, S., Ma, L. (2022). Adaptive mixture of experts learning for generalizable face anti-spoofing. In Proceedings of international conference on multimedia (pp. 6009–6018)

  • Zhu, R., & Li, S. (2022). Crossmatch: Cross-classifier consistency regularization for open-set single domain generalization. In Proceedings of international conference on learning representations (pp. 608–620).

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (Grant No. 2022YFC3310400), in part by the Natural Science Foundation of China (Grant Nos. U23B2054, 62076240, 62102419, and 62276263), in part by the Beijing Municipal Natural Science Foundation (Grant No. 4222054), in part by the Natural Science Foundation of Hunan Province (Grant No. 2024JJ6389), in part by the Scientific Research Foundation of Department of Education of Hunan Province (Grant No. 22B0439), and in part by the Hengyang Science and Technology Plan Project (Grant No. 202330046190).

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  1. Fangling Jiang and Qi Li contributed equally to this study.

Authors and Affiliations

  1. School of Computer Science, University of South China, Hengyang, China

    Fangling Jiang & Bing Liu

  2. New Laboratory of Pattern Recognition (NLPR), MAIS, CASIA, Beijing, China

    Qi Li & Zhenan Sun

  3. School of Artificial Intelligence, UCAS, Beijing, China

    Qi Li & Zhenan Sun

  4. The Laboratory of Cognition and Decision Intelligence for Complex Systems, CASIA, Beijing, China

    Weining Wang

  5. School of Artificial Intelligence, Beijing Normal University, Beijing, China

    Min Ren

  6. OPPO Research Institute, Beijing, China

    Wei Shen

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  1. Fangling Jiang
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Correspondence to Qi Li.

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Jiang, F., Li, Q., Wang, W. et al. Open-Set Single-Domain Generalization for Robust Face Anti-Spoofing. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02129-0

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