{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T21:04:27Z","timestamp":1754600667094,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030913861"},{"type":"electronic","value":"9783030913878"}],"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\/978-3-030-91387-8_19","type":"book-chapter","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T15:05:39Z","timestamp":1637247939000},"page":"293-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Potential Threat of\u00a0Face Swapping to\u00a0eKYC with\u00a0Face Registration and\u00a0Augmented Solution with\u00a0Deepfake Detection"],"prefix":"10.1007","author":[{"given":"Trong-Le","family":"Do","sequence":"first","affiliation":[]},{"given":"Mai-Khiem","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Huy H.","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Minh-Triet","family":"Tran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"key":"19_CR1","unstructured":"Faceswap (2017). https:\/\/github.com\/MarekKowalski\/FaceSwap"},{"key":"19_CR2","unstructured":"Deepfake (2018). https:\/\/github.com\/deepfakes\/faceswap"},{"key":"19_CR3","unstructured":"Terrifying high-tech porn: Creepy deepfake videos are on the rise (2018). https:\/\/www.foxnews.com\/tech\/terrifying-high-tech-porn-creepy-deepfake-videos-are-on-the-rise"},{"key":"19_CR4","unstructured":"Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K., Li, H.: Protecting world leaders against deep fakes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2019)"},{"issue":"7","key":"19_CR5","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","volume":"28","author":"JH Bappy","year":"2019","unstructured":"Bappy, J.H., Simons, C., Nataraj, L., Manjunath, B.S., Roy-Chowdhury, A.K.: Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Trans. Image Process. 28(7), 3286\u20133300 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR6","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-319-92627-8_10","volume-title":"Handbook of Biometric Anti-Spoofing","author":"S Bhattacharjee","year":"2019","unstructured":"Bhattacharjee, S., Mohammadi, A., Anjos, A., Marcel, S.: Recent advances in face presentation attack detection. In: Marcel, S., Nixon, M.S., Fierrez, J., Evans, N. (eds.) Handbook of Biometric Anti-Spoofing. ACVPR, pp. 207\u2013228. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-92627-8_10"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Bonettini, N., Cannas, E.D., Mandelli, S., Bondi, L., Bestagini, P., Tubaro, S.: Video face manipulation detection through ensemble of CNNs. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5012\u20135019 (2021)","DOI":"10.1109\/ICPR48806.2021.9412711"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u20131807. IEEE, Honolulu, HI (July 2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"19_CR9","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-319-92627-8_12","volume-title":"Handbook of Biometric Anti-Spoofing","author":"A Costa-Pazo","year":"2019","unstructured":"Costa-Pazo, A., Vazquez-Fernandez, E., Alba-Castro, J.L., Gonz\u00e1lez-Jim\u00e9nez, D.: Challenges of face presentation attack detection in real scenarios. In: Marcel, S., Nixon, M.S., Fierrez, J., Evans, N. (eds.) Handbook of Biometric Anti-Spoofing. ACVPR, pp. 247\u2013266. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-92627-8_12"},{"key":"19_CR10","unstructured":"Davletshin, A.: (2020). https:\/\/github.com\/ntech-lab\/deepfakedetection-challenge"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: RetinaFace: single-shot multi-level face localisation in the wild. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5202\u20135211. IEEE, Seattle, WA, USA (June 2020)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"19_CR12","unstructured":"Dolhansky, B., et al.: The deepfake detection challenge dataset. CoRR abs\/2006.07397 (2020)"},{"key":"19_CR13","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-319-92627-8_9","volume-title":"Handbook of Biometric Anti-Spoofing","author":"J Hernandez-Ortega","year":"2019","unstructured":"Hernandez-Ortega, J., Fierrez, J., Morales, A., Galbally, J.: Introduction to face presentation attack detection. In: Marcel, S., Nixon, M.S., Fierrez, J., Evans, N. (eds.) Handbook of Biometric Anti-Spoofing. ACVPR, pp. 187\u2013206. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-92627-8_9"},{"key":"19_CR14","unstructured":"Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings. Technical report UM-CS-2010-009, University of Massachusetts, Amherst (2010)"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Khalid, H., Woo, S.S.: Oc-FakeDect: classifying deepfakes using one-class variational autoencoder. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2020)","DOI":"10.1109\/CVPRW50498.2020.00336"},{"issue":"4","key":"19_CR16","first-page":"163","volume":"37","author":"H Kim","year":"2018","unstructured":"Kim, H., et al.: Deep video portraits. ACM Trans. Graph. (TOG) 37(4), 163 (2018)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: DSFD: dual shot face detector. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5055\u20135064. IEEE, Long Beach, CA, USA (June 2019)","DOI":"10.1109\/CVPR.2019.00520"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Face x-ray for more general face forgery detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5000\u20135009 (2020)","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, M.C., Lyu, S.: In ictu oculi: exposing ai created fake videos by detecting eye blinking. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1\u20137 (2018)","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"19_CR20","unstructured":"Li, Y., Lyu, S.: Exposing deepfake videos by detecting face warping artifacts. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019)"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Marcel, S., Nixon, M.S., Fi\u00e9rrez, J., Evans, N.W.D. (eds.): Handbook of Biometric Anti-Spoofing - Presentation Attack Detection, Second Edition. Advances in Computer Vision and Pattern Recognition, Springer, Heidelberg (2019)","DOI":"10.1007\/978-3-319-92627-8"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Lee, W.: The creation and detection of deepfakes: a survey. ACM Comput. Surv. 54(1) (2021)","DOI":"10.1145\/3425780"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Mondal, P.C., Deb, R., Huda, M.N.: Transaction authorization from know your customer (kyc) information in online banking. In: 2016 9th International Conference on Electrical and Computer Engineering (ICECE), pp. 523\u2013526 (2016)","DOI":"10.1109\/ICECE.2016.7853972"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Yamagishi, J., Echizen, I.: Capsule-forensics: using capsule networks to detect forged images and videos. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), pp. 2307\u20132311. IEEE, Brighton, United Kingdom (May 2019)","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Nirkin, Y., Keller, Y., Hassner, T.: Fsgan: subject agnostic face swapping and reenactment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (October 2019)","DOI":"10.1109\/ICCV.2019.00728"},{"key":"19_CR26","unstructured":"Perov, I., et al.: Deepfacelab: a simple, flexible and extensible face swapping framework (2020)"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Transferable deep-cnn features for detecting digital and print-scanned morphed face images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1822\u20131830 (2017)","DOI":"10.1109\/CVPRW.2017.228"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Niessner, M.: FaceForensics++: learning to detect manipulated facial images. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1\u201311. IEEE, Seoul, Korea (South) (October 2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"19_CR29","unstructured":"Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., Natarajan, P.: Recurrent convolutional strategies for face manipulation detection in videos. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 80\u201387. Computer Vision Foundation\/IEEE (2019)"},{"key":"19_CR30","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3859\u20133869. NIPS 2017, Curran Associates Inc., Red Hook, NY, USA (2017)"},{"key":"19_CR31","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Tang, X., Du, D.K., He, Z., Liu, J.: Pyramidbox: a context-assisted single shot face detector. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)","DOI":"10.1007\/978-3-030-01240-3_49"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Thies, J., Zollhofer, M., Stamminger, M., Theobalt, C., Niessner, M.: Face2face: real-time face capture and reenactment of rgb videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)","DOI":"10.1109\/CVPR.2016.262"},{"issue":"4","key":"19_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. 38(4), 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"issue":"1","key":"19_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40854-020-00220-2","volume":"7","author":"JS Wang","year":"2021","unstructured":"Wang, J.S.: Exploring biometric identification in fintech applications based on the modified tam. Financ. Innov. 7(1), 1\u201324 (2021)","journal-title":"Financ. Innov."},{"key":"19_CR36","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., Tang, X.: WIDER FACE: a face detection benchmark. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5525\u20135533. IEEE, Las Vegas, NV, USA (June 2016)","DOI":"10.1109\/CVPR.2016.596"},{"key":"19_CR37","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261\u20138265 (2019)","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"19_CR38","doi-asserted-by":"crossref","unstructured":"Zakharov, E., Shysheya, A., Burkov, E., Lempitsky, V.: Few-shot adversarial learning of realistic neural talking head models. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9458\u20139467. IEEE (October 2019)","DOI":"10.1109\/ICCV.2019.00955"},{"key":"19_CR39","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., Davis, L.S .: Two-stream neural networks for tampered face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (July 2017)","DOI":"10.1109\/CVPRW.2017.229"}],"container-title":["Lecture Notes in Computer Science","Future Data and Security Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91387-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:14:36Z","timestamp":1637540076000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91387-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030913861","9783030913878"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91387-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"19 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","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":"24 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.org\/","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":"168","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":"52","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":"8","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":"31% - 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":"6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}