{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:50:15Z","timestamp":1776441015673,"version":"3.51.2"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030904388","type":"print"},{"value":"9783030904395","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-90439-5_20","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:13:49Z","timestamp":1638454429000},"page":"251-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation"],"prefix":"10.1007","author":[{"given":"Naser","family":"Damer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiran","family":"Raja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"S\u00fc\u00dfmilch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sushma","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fadi","family":"Boutros","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiling","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Kirchbuchner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghavendra","family":"Ramachandra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arjan","family":"Kuijper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"20_CR1","unstructured":"Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: Optimizing the latent space of generative networks. In: ICML. Proceedings of Machine Learning Research, vol. 80, pp. 599\u2013608. PMLR (2018)"},{"key":"20_CR2","volume-title":"Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society","author":"R Bolle","year":"1998","unstructured":"Bolle, R., Pankanti, S.: Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society. Kluwer Academic Publishers, Norwell (1998)"},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.1137\/0916069","volume":"16","author":"R Byrd","year":"1995","unstructured":"Byrd, R., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16, 1190\u20131208 (1995). https:\/\/doi.org\/10.1137\/0916069","journal-title":"SIAM J. Sci. Comput."},{"key":"20_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1007\/978-3-030-12939-2_36","volume-title":"Pattern Recognition","author":"N Damer","year":"2019","unstructured":"Damer, N., et al.: Detecting face morphing attacks by analyzing the directed distances of facial landmarks shifts. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 518\u2013534. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12939-2_36"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Damer, N., Boutros, F., Saladie, A.M., Kirchbuchner, F., Kuijper, A.: Realistic dreams: cascaded enhancement of GAN-generated images with an example in face morphing attacks. In: BTAS, pp. 1\u201310. IEEE (2019)","DOI":"10.1109\/BTAS46853.2019.9185994"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Damer, N., Grebe, J.H., Zienert, S., Kirchbuchner, F., Kuijper, A.: On the generalization of detecting face morphing attacks as anomalies: novelty vs. outlier detection. In: BTAS, pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/BTAS46853.2019.9185995"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Damer, N., Saladie, A.M., Braun, A., Kuijper, A.: MorGAN: recognition vulnerability and attack detectability of face morphing attacks created by generative adversarial network. In: BTAS, pp. 1\u201310. IEEE (2018)","DOI":"10.1109\/BTAS.2018.8698563"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Damer, N., et al.: To detect or not to detect: the right faces to morph. In: ICB, pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/ICB45273.2019.8987316"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Damer, N., Zienert, S., Wainakh, Y., Saladie, A.M., Kirchbuchner, F., Kuijper, A.: A multi-detector solution towards an accurate and generalized detection of face morphing attacks. In: FUSION, pp. 1\u20138. IEEE (2019)","DOI":"10.23919\/FUSION43075.2019.9011378"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: CVPR, pp. 4690\u20134699. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Ferrara, M., Franco, A., Maltoni, D.: The magic passport. In: IJCB, pp. 1\u20137. IEEE (2014)","DOI":"10.1109\/BTAS.2014.6996240"},{"issue":"4","key":"20_CR12","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1109\/TIFS.2017.2777340","volume":"13","author":"M Ferrara","year":"2018","unstructured":"Ferrara, M., Franco, A., Maltoni, D.: Face demorphing. IEEE Trans. Inf. Forensics Secur. 13(4), 1008\u20131017 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Fu, B., Spiller, N., Chen, C., Damer, N.: The effect of face morphing on face image quality. In: BIOSIG. LNI, Gesellschaft f\u00fcr Informatik e.V. (2021)","DOI":"10.1109\/BIOSIG52210.2021.9548302"},{"key":"20_CR14","unstructured":"GmbH, C.S.: Facevacs technology - version 9.4.2 (2020). https:\/\/www.cognitec.com\/facevacs-technology.html"},{"key":"20_CR15","unstructured":"Patrick, G., Mei, N., Kayee, H.: Ongoing face recognition vendor test (FRVT). NIST Interagency Report (2020)"},{"key":"20_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-46487-9_6","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Y Guo","year":"2016","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87\u2013102. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_6"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"20_CR18","unstructured":"International Civil Aviation Organization, ICAO: Machine readable passports - part 9 - deployment of biometric identification and electronic storage of data in eMRTDs. Civil Aviation Organization (ICAO) (2015)"},{"key":"20_CR19","unstructured":"International Organization for Standardization: ISO\/IEC DIS 30107-3:2016: Information Technology - Biometric presentation attack detection - P. 3: Testing and reporting (2017)"},{"key":"20_CR20","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: ICLR. OpenReview.net (2018)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: CVPR, pp. 4401\u20134410. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: CVPR, pp. 6738\u20136746. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"20_CR23","unstructured":"Markets and Markets: Facial Recognition Market by Component (Software Tools and Services), Technology, Use Case (Emotion Recognition, Attendance Tracking and Monitoring, Access Control, Law Enforcement), End-User, and Region - Global Forecast to 2022. Report, November 2017"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Massoli, F.V., Carrara, F., Amato, G., Falchi, F.: Detection of face recognition adversarial attacks. Comput. Vis. Image Underst. 202, 103103 (2021)","DOI":"10.1016\/j.cviu.2020.103103"},{"key":"20_CR25","unstructured":"NIST: FRVT morph web site. NIST Interagency Report (2020)"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Phillips, P.J., et al.: Overview of the face recognition grand challenge. In: CVPR (1), pp. 947\u2013954. IEEE Computer Society (2005)","DOI":"10.1109\/CVPR.2005.268"},{"issue":"1","key":"20_CR27","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/TBIOM.2020.3022007","volume":"3","author":"L Qin","year":"2021","unstructured":"Qin, L., Peng, F., Venkatesh, S., Ramachandra, R., Long, M., Busch, C.: Low visual distortion and robust morphing attacks based on partial face image manipulation. IEEE Trans. Biom. Behav. Identity Sci. 3(1), 72\u201388 (2021)","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Face morphing versus face averaging: vulnerability and detection. In: IJCB, pp. 555\u2013563. IEEE (2017)","DOI":"10.1109\/BTAS.2017.8272742"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Ramachandra, R., Venkatesh, S., Raja, K.B., Busch, C.: Towards making morphing attack detection robust using hybrid scale-space colour texture features. In: ISBA, pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/ISBA.2019.8778488"},{"issue":"6","key":"20_CR30","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1049\/iet-bmt.2019.0206","volume":"9","author":"U Scherhag","year":"2020","unstructured":"Scherhag, U., Kunze, J., Rathgeb, C., Busch, C.: Face morph detection for unknown morphing algorithms and image sources: a multi-scale block local binary pattern fusion approach. IET Biom. 9(6), 278\u2013289 (2020)","journal-title":"IET Biom."},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Scherhag, U., et al.: Biometric systems under morphing attacks: assessment of morphing techniques and vulnerability reporting. In: BIOSIG. LNI, vol. P-270, pp. 149\u2013159. GI\/IEEE (2017)","DOI":"10.23919\/BIOSIG.2017.8053499"},{"key":"20_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Venkatesh, S., Ramachandra, R., Raja, K.B., Busch, C.: Single image face morphing attack detection using ensemble of features. In: FUSION, pp. 1\u20136. IEEE (2020)","DOI":"10.23919\/FUSION45008.2020.9190629"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Venkatesh, S., Zhang, H., Ramachandra, R., Raja, K.B., Damer, N., Busch, C.: Can GAN generated morphs threaten face recognition systems equally as landmark based morphs? - vulnerability and detection. In: IWBF, pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/IWBF49977.2020.9107970"},{"key":"20_CR35","unstructured":"Zhang, H., Venkatesh, S., Ramachandra, R., Raja, K.B., Damer, N., Busch, C.: MIPGAN - generating robust and high quality morph attacks using identity prior driven GAN. CoRR abs\/2009.01729 (2020)"},{"issue":"10","key":"20_CR36","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90439-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T16:00:34Z","timestamp":1726243234000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90439-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030904388","9783030904395"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90439-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isvc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isvc.net\/?utm_source=researchbib","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}