{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:39:13Z","timestamp":1773157153612,"version":"3.50.1"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030953973","type":"print"},{"value":"9783030953980","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-95398-0_4","type":"book-chapter","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T18:03:42Z","timestamp":1642701822000},"page":"47-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exposing Deepfake Videos with Spatial, Frequency and Multi-scale Temporal Artifacts"],"prefix":"10.1007","author":[{"given":"Yongjian","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeqiong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beibei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Yan, J., et al.: Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16317\u201316326 (2021)","DOI":"10.1109\/CVPR46437.2021.01605"},{"issue":"10","key":"4_CR2","doi-asserted-by":"publisher","first-page":"e3","DOI":"10.23915\/distill.00003","volume":"1","author":"A Odena","year":"2016","unstructured":"Odena, A., Dumoulin, V., Olah, C.: Deconvolution and checkerboard artifacts. Distill 1(10), e3 (2016)","journal-title":"Distill"},{"issue":"10","key":"4_CR3","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1109\/TSP.2005.855406","volume":"53","author":"AC Popescu","year":"2005","unstructured":"Popescu, A.C., Farid, H.: Exposing digital forgeries in color filter array interpolated images. IEEE Trans. Sig. Process. 53(10), 3948\u20133959 (2005)","journal-title":"IEEE Trans. Sig. Process."},{"key":"4_CR4","unstructured":"Frank, J., Eisenhofer, T., Sch\u00f6nherr, L., et al.: Leveraging frequency analysis for deep fake image recognition. In: International Conference on Machine Learning, pp. 3247\u20133258. PMLR (2020)"},{"issue":"3","key":"4_CR5","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1109\/TBIOM.2021.3065735","volume":"3","author":"B Han","year":"2021","unstructured":"Han, B., Han, X., Zhang, H., et al.: Fighting fake news: two stream network for deepfake detection via learnable SRM. IEEE Trans. Biom. Behav. Identity Sci. 3(3), 320\u2013331 (2021)","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Deep spatial gradient and temporal depth learning for face anti-spoofing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00509"},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"4234","DOI":"10.1109\/TIFS.2021.3102487","volume":"16","author":"J Yang","year":"2021","unstructured":"Yang, J., Li, A., Xiao, S., et al.: MTD-Net: learning to detect deepfakes images by multi-scale texture difference. IEEE Trans. Inf. Forensics Secur. 16, 4234\u20134245 (2021)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"11","key":"4_CR8","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR9","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"4_CR10","unstructured":"Korshunov, P., Marcel, S.: Deepfakes: a new threat to face recognition? Assessment and detection. arXiv preprint arXiv:1812.08685 (2018)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., et al.: FaceForensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1\u201311 (2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Khodabakhsh, A., Ramachandra, R., Raja, K., et al.: Fake face detection methods: can they be generalized? In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1\u20136. IEEE (2018)","DOI":"10.23919\/BIOSIG.2018.8553251"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., et al.: MesoNet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/WIFS.2018.8630761"},{"issue":"11","key":"4_CR14","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1109\/TIFS.2018.2825953","volume":"13","author":"B Bayar","year":"2018","unstructured":"Bayar, B., Stamm, M.C.: Constrained convolutional neural networks: a new approach towards general purpose image manipulation detection. IEEE Trans. Inf. Forensics Secur. 13(11), 2691\u20132706 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Tariq, S., Lee, S., Kim, H., et al.: Detecting both machine and human created fake face images in the wild. In: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security, pp. 81\u201387 (2018)","DOI":"10.1145\/3267357.3267367"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Li, X., Lang, Y., Chen, Y., et al.: Sharp multiple instance learning for deepfake video detection. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1864\u20131872 (2020)","DOI":"10.1145\/3394171.3414034"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., et al.: On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5781\u20135790 (2020)","DOI":"10.1109\/CVPR42600.2020.00582"}],"container-title":["Lecture Notes in Computer Science","Digital Forensics and Watermarking"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95398-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T18:10:26Z","timestamp":1642702226000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95398-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030953973","9783030953980"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95398-0_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWDW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Digital Watermarking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwdw2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iwdw.site\/","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":"32","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":"18","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":"56% - 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":"2.3","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":"2.1","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)"}},{"value":"The conference was held virtuallry due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}