Face Anti-spoofing Based on Domain Synthesis and Contrastive Learning

doi:10.3969/j.issn.1006-2475.2025.07.001

Abstract

Abstract: Abstract: Face anti-spoofing （FAS） is an important mean to guarantee the security of face recognition systems. Existing FAS methods have poor generalization in cross-dataset testing scenarios， which leads to a drastic performance degradation. To this way， this paper proposes a FAS method based on domain synthesis and contrastive learning. The proposed method mainly contains two novel modules: domain synthesis module and contrastive learning module. The former randomly swaps local regions of face images in different source domains at the image level to generate pseudo-source domain samples. Then， the above face images are reconstructed at the feature-level by exchanging the local features of the corresponding positions reconstructed at the image-level. By maximizing the similarity of the reconstructed sample’s features and the reconstructed features， this module can ensure the stability of the generated pseudo source domain while expanding the number of samples and attack types. This provides a solid data foundation for the proposed method to learn generalized feature spaces. The later minimizes the intra-class distance of the real face representation and maximizes the inter-class distance between the real face and spoof faces. Meanwhile， this module maximizes the inter-class distance between the real face and the reconstructed samples. This process effectively promotes intra-class compactness of the real face and ensures that the proposed method can learn a good decision curve. The proposed method is trained and tested on four publicly available face live detection datasets CASIA-FASD， Replay-Attack， MSU-MFSD， and OULU-NPU， and the experimental results show that the proposed method has a good generalization performance in cross-dataset testing scenarios.

Key words: Key words: face anti-spoofing, pseudo source domain, contrastive learning, domain generalization

CLC Number:

TP391

ZHANG Shanlu1, ZHANG Wei2. Face Anti-spoofing Based on Domain Synthesis and Contrastive Learning[J]. Computer and Modernization, 2025, 0(07): 1-8.

References

［1］李刚，高政. 人脸识别理论研究进展［J］. 计算机与现代化， 2003（3）:1-6.
［2］ DÉNIZ O， BUENO G， SALIDO J， et al. Face recognition using histograms of oriented gradients［J］. Pattern Recognition Letters， 2011，32（12）:1598-1603.
［3］ LIU A J， TAN Z C， WAN J， et al. Face Anti-spoofing via adversarial cross-modality translation［J］. IEEE Transactions on Information Forensics and Security， 2021，16:2759
-2772.
［4］ BOULKENAFET Z， KOMULAINEN J， HADID A. Face spoofing detection using colour texture analysis［J］. IEEE Transactions on Information Forensics and Security， 2016，11（8）:1818-1830.
［5］ LIU A J， ZHAO C X， YU Z T， et al. Contrastive context-aware learning for 3D high-fidelity mask face presentation attack detection［J］. IEEE Transactions on Information Forensics and Security， 2022，17:2497-2507.
［6］ WANG W H， WEN F， ZHENG H Y， et al. Conv-MLP: A convolution and MLP mixed model for multimodal face anti-spoofing［J］. IEEE Transactions on Information Forensics and Security， 2022，17:2284-2297.
［7］ YU Z T， WAN J， QIN Y X， et al. NAS-FAS: Static-dynamic central difference network search for face anti-spoofing［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2021，43（9）:3005-3023.
［8］ ZHANG D A， LI C M， LI H Q， et al. Rethinking alignment and uniformity in unsupervised image semantic segmentation［C］// Proceedings of the AAAI Conference on Artificial Intelligence. AAAI， 2023，37（9）:11192-11200.
［9］ YUN S K， LEE H K， KIM J Y， et al. Patch-level representation learning for self-supervised vision Transformers［C］// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2022:8344-8353.
［10］ HUANG S， CHENG W H， CHENG R. Single patch based 3D high-fidelity mask face anti-spoofing［C］// 2021 IEEE/CVF International Conference on Computer Vision Workshops （ICCVW）. IEEE， 2021:842-845.
［11］ TIRUNAGARI S， POH N， WINDRIDGE D， et al. Detection of face spoofing using visual dynamics［J］. IEEE Transactions on Information Forensics and Security， 2015，10（4）:762-777.
［12］ ALSUFYANI N， ALI A， HOQUE S， et al. Biometric presentation attack detection using gaze alignment［C］// 2018 IEEE 4th International Conference on Identity， Security， and Behavior Analysis （ISBA）. IEEE， 2018:1-8.
［13］ KOLLREIDER K， FRONTHALER H， FARAJ M I， et al. Real-time face detection and motion analysis with application in “liveness” assessment［J］. IEEE Transactions on Information Forensics and Security， 2007，2（3）:548-558.
［14］ PAN G， SUN L， WU Z H， et al. Eyeblink-based anti-spoofing in face recognition from a generic webcamera［C］// 2007 IEEE 11th International Conference on Computer Vision. IEEE， 2007:1-8.
［15］施新岚，黄俊，黄洋，等. 基于Gabor纹理增强的人脸活体检测算法［J］. 重庆邮电大学学报（自然科学版）， 2020，32（6）：1023-1030.
［16］ LI H L， WANG S Q， KOT A C. Face spoofing detection with image quality regression［C］// 2016 6th International Conference on Image Processing Theory， Tools and Applications （IPTA）. IEEE， 2016. DOI: 10.1109/IPTA.2016.78
21027.
［17］ LIM W Q. The discrete shearlet transform: A new directional transform and compactly supported shearlet frames［J］. IEEE Transactions on Image Processing， 2010，19（5）:1166-1180.
［18］ WU Y Q， TAO D P， LUO Y， et al. Covered style mining via generative adversarial networks for face anti-spoofing［J］. Pattern Recognition， 2022，132:108957.
［19］ YU B Y， LU J W， LI X， et al. Salience-aware face presentation attack detection via deep reinforcement learning［J］. IEEE Transactions on Information Forensics and Security， 2022，17:413-427.
［20］ CAI R Z， LI H L， WANG S Q， et al. DRL-FAS: A novel framework based on deep reinforcement learning for face anti-spoofing［J］. IEEE Transactions on Information Forensics and Security， 2021，16:937-951.
［21］ WANG Z， WANG Q C， DENG W H， et al. Face anti-spoofing using Transformers with relation-aware mechanism［J］. IEEE Transactions on Biometrics， Behavior， and Identity Science， 2022，4（3）:439-450.
［22］王宏飞，程鑫，赵祥模，等. 光流与纹理特征融合的人脸活体检测算法［J］. 计算机工程与应用， 2022，58（6）:170-176.
［23］ HASAN M R， MAHMUD S M H， LI X Y. Face anti-spoofing using texture-based techniques and filtering methods［C］// 2019 3rd International Conference on Machine Vision and Information Technology （CMVIT 2019）. IOP， 2019，1229. DOI: 10.1088/1742-6596/1229/1/012044.
［24］ WANG G Q， HAN H， SHAN S G， et al. Cross-domain face presentation attack detection via multi-domain disentangled representation learning［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2020:6677-6686.
［25］ SHAO R， LAN X Y， LI J W， et al. Multi-adversarial discriminative deep domain generalization for face presentation attack detection［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2019:10023-10031.
［26］ JIA Y P， ZHANG J， SHAN S G， et al. Single-side domain generalization for face anti-spoofing［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2020:8481-8490.
［27］ MAO J J， WENG B， HUANG T Q， et al. Research on multimodality face antispoofing model based on adversarial attacks［J］. Security and Communication Networks， 2021， 2021. DOI: 10.1155/2021/367033.
［28］田野，项世军. 基于LBP和多层DCT的人脸活体检测算法［J］. 计算机研究与发展， 2018，55（3）:643-650.
［29］ YANG K D， ZHANG Z Y， TIAN Y L， et al. A secure authentication framework to guarantee the traceability of avatars in metaverse［J］. IEEE Transactions on Information Forensics and Security， 2023，18:3817-3832.
［30］ YANG J W， LEI Z， LI S Z. Learn convolutional neural network for face anti-spoofing［J］. arXiv preprint arXiv:1408.5601， 2014.
［31］ YU Z T， ZHAO C X， WANG Z Z， et al. Searching central difference convolutional networks for face anti-spoofing［C］// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2020:5294-5304.
［32］ SUN Y Y， LIU Y J， LIU X M， et al. Rethinking domain generalization for face anti-spoofing: Separability and alignment［Z］. arXiv preprint arXiv:2303.13662， 2023.
［33］ ZHANG Z W， YAN J J， LIU S F， et al. A face antispoofing database with diverse attacks［C］// 2012 5th IAPR International Conference on Biometrics （ICB）. IEEE， 2012:26-31.
［34］ WEN D， HAN H， JAIN A K. Face spoof detection with image distortion analysis［J］. IEEE Transactions on Information Forensics and Security， 2015，10（4）:746-761.
［35］ CHINGOVSKA I， ANJOS A， MARCEL S. On the effectiveness of local binary patterns in face anti-spoofing［C］//Proceedings of the 2012 International Conference of Biometrics Special Interest Group （BIOSIG）. IEEE， 2012:1-7.
［36］ BOULKENAFET Z， KOMULAINEN J， LI L， et al. OULU-NPU: A mobile face presentation attack database with real-world variations［C］// 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition （FG 2017）. IEEE， 2017:612-618.
［37］ HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. IEEE， 2016:770-778.
［38］ FENG H C， HONG Z B， YUE H X， et al. Learning generalized spoof cues for face anti-spoofing［Z］. arXiv preprint arXiv:2005.03922， 2020.
［39］ YANG X Y， YE J W， WANG X C. Factorizing knowledge in neural networks［J］. arXiv preprint arXiv:2207.03337， 2022.
［40］ LIU Y J， JOURABLOO A， LIU X M. Learning deep models for face anti-spoofing: Binary or auxiliary supervision［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2018:389-398.
［41］ SHAO R， LAN X Y， YUEN P C. Regularized fine-grained meta face anti-spoofing［C］// Proceedings of the AAAI Conference on Artificial Intelligence. AAAI， 2020，34:11974-11981.
［42］ LIU S B， ZHANG K Y， YAO T P， et al. Adaptive normalized representation learning for generalizable face anti-spoofing［C］// Proceedings of the 29th ACM International Conference on Multimedia. ACM， 2021:1469-1477.
［43］ WANG C Y， LU Y D， YANG S T， et al. PatchNet: A simple face anti-spoofing framework via fine-grained patch recognition［C］// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2022:20249-20258.
［44］ WANG Z ， WANG Z Z， YU Z T， et al. Domain generalization via shuffled style assembly for face anti-spoofing［C］// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2022: 4113-4123.
［45］ ZHOU Q Y， ZHANG K Y， YAO T P， et al. Instance-aware domain generalization for face anti-spoofing［C］// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2023:20453-20463.
［46］ CHEN Z H， YAO T P， SHENG K K， et al. Generalizable representation learning for mixture domain face anti-spoofing［C］// Proceedings of the AAAI Conference on Artificial Intelligence. AAAI， 2021，35（2）:1132-1139.
［47］ ZHANG K Y， YAO T P， ZHANG J， et al. Face anti-spoofing via disentangled representation learning［J］. arXiv preprint arXiv:2008.08250， 2020.
［48］ YU Z T， LI X B， NIU X S， et al. Face anti-spoofing with human material perception［C］// Proceedings of the 2020 16th European Conference on Computer Vision. Springer， 2020: 557-575.
［49］ YU Z T， QIN Y X， ZHAO H S， et al. Dual-cross central difference network for face anti-spoofing［J］. arXiv preprint arXiv:2105.01290， 2021.

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