{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:21:03Z","timestamp":1771258863055,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819561223","type":"print"},{"value":"9789819561230","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-6123-0_47","type":"book-chapter","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T15:43:54Z","timestamp":1771256634000},"page":"504-514","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Domain Generalization in\u00a0Face Anti-Spoofing Based on\u00a0Vision-Language Semantic Awareness"],"prefix":"10.1007","author":[{"given":"Fengmei","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanlong","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"47_CR1","first-page":"373","volume":"9218","author":"J Yang","year":"2014","unstructured":"Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. Comput. Sci. 9218, 373\u2013384 (2014)","journal-title":"Comput. Sci."},{"key":"47_CR2","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1109\/TIFS.2020.3029879","volume":"16","author":"D Deb","year":"2020","unstructured":"Deb, D., Jain, A.K.: Look locally infer globally: a generalizable face anti-spoofing approach. IEEE Trans. Inf. Forensics Secur. 16, 1143\u20131157 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"47_CR3","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zhao, C., Wang, Z., et al.: Searching central difference convolutional networks for face anti-spoofing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5295\u20135305 (2020)","DOI":"10.1109\/CVPR42600.2020.00534"},{"key":"47_CR4","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/TIFS.2020.3002390","volume":"16","author":"G Wang","year":"2020","unstructured":"Wang, G., Han, H., Shan, S., et al.: Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection. IEEE Trans. Inf. Forensics Secur. 16, 56\u201369 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"7","key":"47_CR5","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1007\/s11263-023-01778-x","volume":"131","author":"F Jiang","year":"2023","unstructured":"Jiang, F., Li, Q., Liu, P., et al.: Adversarial learning domain-invariant conditional features for robust face anti-spoofing. Int. J. Comput. Vis. 131(7), 1680\u20131703 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Yuan, S., Dong, J., Li, Y.: Where the devil hides: deepfake detectors can no longer be trusted. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8764\u20138774 (2025)","DOI":"10.1109\/CVPR52734.2025.00819"},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yao, T., Sheng, K., et al.: Generalizable representation learning for mixture domain face anti-spoofing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 2, pp. 1132\u20131139 (2021)","DOI":"10.1609\/aaai.v35i2.16199"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, J., Bian, Y., et al.: Self-domain adaptation for face anti-spoofing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 4, pp. 2746\u20132754 (2021)","DOI":"10.1609\/aaai.v35i4.16379"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Sun, Y., Liu, Y., Liu, X., et al.: Rethinking domain generalization for face anti-spoofing: separability and alignment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24563\u201324574 (2023)","DOI":"10.1109\/CVPR52729.2023.02353"},{"key":"47_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Zhang, K., Yao, T., et al.: Instance-aware domain generalization for face anti-spoofing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20453\u201320463 (2023)","DOI":"10.1109\/CVPR52729.2023.01959"},{"key":"47_CR11","doi-asserted-by":"publisher","first-page":"3093","DOI":"10.1109\/TIFS.2024.3356234","volume":"19","author":"F Jiang","year":"2024","unstructured":"Jiang, F., Liu, Y., Si, H., et al.: Cross-scenario unknown-aware face anti-spoofing with evidential semantic consistency learning. IEEE Trans. Inf. Forensics Secur. 19, 3093\u20133108 (2024)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"11","key":"47_CR12","doi-asserted-by":"publisher","first-page":"5151","DOI":"10.1007\/s11263-024-02129-0","volume":"132","author":"F Jiang","year":"2024","unstructured":"Jiang, F., Li, Q., Wang, W., et al.: Open-set single-domain generalization for robust face anti-spoofing. Int. J. Comput. Vis. 132(11), 5151\u20135172 (2024)","journal-title":"Int. J. Comput. Vis."},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Cai, R., Yu, Z., Kong, C., et al.: S-adapter: generalizing vision transformer for face anti-spoofing with statistical tokens. IEEE Trans. Inf. Forensics Secur. (2024)","DOI":"10.1109\/TIFS.2024.3420699"},{"key":"47_CR14","doi-asserted-by":"crossref","unstructured":"Cui, X., Li, Y., Zhu, D., et al.: Forensics adapter: unleashing clip for generalizable face forgery detection. arXiv preprint arXiv:2411.19715 (2024)","DOI":"10.1109\/CVPR52734.2025.01789"},{"issue":"11","key":"47_CR15","doi-asserted-by":"publisher","first-page":"5217","DOI":"10.1007\/s11263-024-02055-1","volume":"132","author":"Z Yu","year":"2024","unstructured":"Yu, Z., Cai, R., Cui, Y., et al.: Rethinking vision transformer and masked autoencoder in multimodal face anti-spoofing. Int. J. Comput. Vis. 132(11), 5217\u20135238 (2024)","journal-title":"Int. J. Comput. Vis."},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"Cui, X., Li, Y., Luo, A., et al.: Forensics adapter: adapting CLIP for generalizable face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19207\u201319217 (2025)","DOI":"10.1109\/CVPR52734.2025.01789"},{"issue":"9","key":"47_CR17","doi-asserted-by":"publisher","first-page":"3005","DOI":"10.1109\/TPAMI.2020.3036338","volume":"43","author":"Z Yu","year":"2020","unstructured":"Yu, Z., Wan, J., Qin, Y., et al.: NAS-FAS: static-dynamic central difference network search for face anti-spoofing. IEEE Trans. Pattern Anal. Mach. Intell. 43(9), 3005\u20133023 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, K., Yao, T., et al.: Dual reweighting domain generalization for face presentation attack detection. arXiv preprint arXiv:2106.16128 (2021)","DOI":"10.24963\/ijcai.2021\/120"},{"key":"47_CR19","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, K., Yao, T., et al.: Adaptive normalized representation learning for generalizable face anti-spoofing. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1469\u20131477 (2021)","DOI":"10.1145\/3474085.3475279"},{"key":"47_CR20","doi-asserted-by":"crossref","unstructured":"Wang, C., Lu, Y., Yang, S., et al.: PatchNet: a simple face anti-spoofing framework via fine-grained patch recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20281\u201320290 (2022)","DOI":"10.1109\/CVPR52688.2022.01964"},{"key":"47_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Z., Yu, Z., et al.: Domain generalization via shuffled style assembly for face anti-spoofing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4123\u20134133 (2022)","DOI":"10.1109\/CVPR52688.2022.00409"},{"key":"47_CR22","doi-asserted-by":"crossref","unstructured":"Yang, J., Yu, Z., Ni, X., et al.: Generalized face anti-spoofing via finer domain partition and disentangling liveness-irrelevant factors. In: ECAI 2024, pp. 274\u2013281. IOS Press (2024)","DOI":"10.3233\/FAIA240498"},{"key":"47_CR23","unstructured":"Radford, A., Kim, J.W., Hallacy, C., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Machine Learning, pp. 8748\u20138763 (2021)"},{"issue":"9","key":"47_CR24","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., et al.: Learning to prompt for vision-language models. Int. J. Comput. Vis. 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vis."},{"key":"47_CR25","doi-asserted-by":"crossref","unstructured":"Srivatsan, K., Naseer, M., Nandakumar, K.: Flip: Cross-domain face anti-spoofing with language guidance. In: Proceedings of the IEEE\/CVF Conference on Computer Vision, pp. 19685\u201319696 (2023)","DOI":"10.1109\/ICCV51070.2023.01803"},{"key":"47_CR26","doi-asserted-by":"crossref","unstructured":"Fang, H., Liu, A., Jiang, N., et al.: VL-FAS: Domain generalization via vision-language model for face anti-spoofing. In: Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP), pp. 4770\u20134774 (2024)","DOI":"10.1109\/ICASSP48485.2024.10448156"},{"key":"47_CR27","doi-asserted-by":"crossref","unstructured":"Guo, J., Liu, H., Luo, Y., et al.: Style-conditional prompt token learning for generalizable face anti-spoofing. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 994\u20131003 (2024)","DOI":"10.1145\/3664647.3680857"},{"key":"47_CR28","doi-asserted-by":"crossref","unstructured":"Liu, A., Xue, S., Gan, J., et al.: CFPL-FAS: class free prompt learning for generalizable face anti-spoofing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 222\u2013232 (2024)","DOI":"10.1109\/CVPR52733.2024.00029"},{"key":"47_CR29","doi-asserted-by":"crossref","unstructured":"Guo, J., Liu, A., Diao, Y., et al.: Domain generalization for face anti-spoofing via content-aware composite prompt engineering. arXiv preprint arXiv:2504.04470 (2025)","DOI":"10.1109\/TMM.2025.3618575"},{"key":"47_CR30","doi-asserted-by":"crossref","unstructured":"Lin, X., Liu, A., Yu, Z., et al.: Reliable and balanced transfer learning for generalized multimodal face anti-spoofing. IEEE Trans. Pattern Anal. Mach. Intell. (2025)","DOI":"10.1109\/TPAMI.2025.3573785"},{"issue":"1","key":"47_CR31","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s44267-025-00079-w","volume":"3","author":"Y Shi","year":"2025","unstructured":"Shi, Y., Gao, Y., Lai, Y., et al.: Shield: an evaluation benchmark for face spoofing and forgery detection with multimodal large language models. Vis. Intell. 3(1), 9 (2025)","journal-title":"Vis. Intell."},{"key":"47_CR32","unstructured":"Liu, A., Yuan, H., Guo, X., et al.: Benchmarking unified face attack detection via hierarchical prompt tuning. arXiv preprint arXiv:2505.13327 (2025)"},{"key":"47_CR33","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, N., Liu, A., et al.: FA$$^{3}$$-CLIP: frequency-aware cues fusion and attack-agnostic prompt learning for unified face attack detection. arXiv preprint arXiv:2504.00454 (2025)","DOI":"10.1109\/TIFS.2025.3593167"},{"key":"47_CR34","doi-asserted-by":"crossref","unstructured":"Liu, A., Ma, H., Zheng, J., et al.: FM-CLIP: flexible modal CLIP for face anti-spoofing. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 8228\u20138237 (2024)","DOI":"10.1145\/3664647.3680856"},{"key":"47_CR35","first-page":"34892","volume":"36","author":"H Liu","year":"2023","unstructured":"Liu, H., Li, C., Wu, Q., et al.: Visual instruction tuning. Adv. Neural. Inf. Process. Syst. 36, 34892\u201334916 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"47_CR36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"47_CR37","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Li, L., et al.: OULU-NPU: a mobile face presentation attack database with real-world variations. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 612\u2013618 (2017)","DOI":"10.1109\/FG.2017.77"},{"key":"47_CR38","unstructured":"Chingovska, I., Anjos, A., Marcel, S.: On the effectiveness of local binary patterns in face anti-spoofing. In: BIOSIG, pp. 1\u20137 (2012)"},{"issue":"4","key":"47_CR39","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1109\/TIFS.2015.2400395","volume":"10","author":"D Wen","year":"2015","unstructured":"Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746\u2013761 (2015)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"47_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yan, J., Liu, S., et al.: A face antispoofing database with diverse attacks. In: IAPR International Conference on Biometrics (ICB), pp. 26\u201331 (2012)","DOI":"10.1109\/ICB.2012.6199754"},{"key":"47_CR41","doi-asserted-by":"crossref","unstructured":"Chefer, H., Gur, S., Wolf, L.: Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision, pp. 397\u2013406 (2021)","DOI":"10.1109\/ICCV48922.2021.00045"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6123-0_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T15:44:01Z","timestamp":1771256641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6123-0_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819561223","9789819561230"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6123-0_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"17 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanchang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccbr2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccbr99.cn\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}