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Towards Data-Centric Face Anti-spoofing: Improving Cross-Domain Generalization via Physics-Based Data Synthesis

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Abstract

Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. While recent FAS works are mainly model-centric, focusing on developing domain generalization algorithms for improving cross-domain performance, data-centric research for face anti-spoofing, improving generalization from data quality and quantity, is largely ignored. Therefore, our work starts with data-centric FAS by conducting a comprehensive investigation from the data perspective for improving cross-domain generalization of FAS models. More specifically, at first, based on physical procedures of capturing and recapturing, we propose task-specific FAS data augmentation (FAS-Aug), which increases data diversity by synthesizing data of artifacts, such as printing noise, color distortion, moiré pattern, etc. Our experiments show that using our FAS augmentation can surpass traditional image augmentation in training FAS models to achieve better cross-domain performance. Nevertheless, we observe that models may rely on the augmented artifacts, which are not environment-invariant, and using FAS-Aug may have a negative effect. As such, we propose Spoofing Attack Risk Equalization (SARE) to prevent models from relying on certain types of artifacts and improve the generalization performance. Last but not least, our proposed FAS-Aug and SARE with recent Vision Transformer backbones can achieve state-of-the-art performance on the FAS cross-domain generalization protocols. The implementation is available at https://github.com/RizhaoCai/FAS-Aug.

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Data availability

The ten datasets used in this paper are publically available (i.e. SiW (Liu et al., 2018), ROSE-YOUTU(Li et al., 2018b), OULU-NPU (Boulkenafet et al., 2017), CASIA FASD(Zhang et al., 2012), IDIAP Replay-Attack (Chingovska et al., 2012), MSU MFSD (Wen et al., 2015), CASIA-SURF CeFA (Liu et al., 2021), CASIA-SURF (Zhang et al., 2020), CASIA-SURF HiFi Mask (Liu et al., 2022), HKBU Marvs V2 (Liu et al., 2016)). These datasets can be found in third-party institutions, including Idiap Research Institute, Hong Kong Baptist University, Institute of Automation of Chinese Academy of Sciences, Tencent Youtu Research, and Michigan State University.

Notes

  1. https://github.com/RizhaoCai/FAS-Aug

  2. https://github.com/taylover-pei/SSDG-CVPR2020.

  3. https://github.com/wangzhuo2019/SSAN.

  4. https://github.com/koushiksrivats/FLIP.

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Acknowledgements

This work was carried out at Rapid-Rich Object Search (ROSE) Lab, School of Electrical & Electronic Engineering, Nanyang Technological University. This research is supported by the NTU-PKU Joint Research Institute (a collaboration between the Nanyang Technological University and Peking University that is sponsored by a donation from the Ng Teng Fong Charitable Foundation). This work is also partly by the Basic and Frontier Research Project of PCL, the Major Key Project of PCL, partly by Guangdong Basic and Applied Basic Research Foundation (Grant No. 2024A1515010454 &2023A1515140037), National Natural Science Foundation of China under Grant 62306061, and Chow Sang Sang Group Research Fund under grant DON-RMG 9229161.

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  1. Rizhao Cai and Cecelia Soh have contributed equal to this work.

Authors and Affiliations

  1. ROSE Lab, Nanyang Technological University, Singapore, Singapore

    Rizhao Cai, Cecelia Soh & Alex C. Kot

  2. Pengcheng Laboratory, Shenzhen, China

    Wenhan Yang

  3. Great Bay University, Dongguan, China

    Zitong Yu

  4. City University of Hong Kong, Hong Kong, China

    Haoliang Li

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Communicated by R. Cai, C. Soh: Yasushi Yagi.

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Cai, R., Soh, C., Yu, Z. et al. Towards Data-Centric Face Anti-spoofing: Improving Cross-Domain Generalization via Physics-Based Data Synthesis. Int J Comput Vis 133, 1689–1710 (2025). https://doi.org/10.1007/s11263-024-02240-2

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  • DOI: https://doi.org/10.1007/s11263-024-02240-2