{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T21:26:24Z","timestamp":1753737984894,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031581731"},{"type":"electronic","value":"9783031581748"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58174-8_41","type":"book-chapter","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:02:39Z","timestamp":1719910959000},"page":"491-502","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Effective CNN-Based Approach for\u00a0Synthetic Face Image Detection in\u00a0Pre-social and\u00a0Post-social Media Context"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3859-4257","authenticated-orcid":false,"given":"Protyay","family":"Dey","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9161-9509","authenticated-orcid":false,"given":"Abhilasha S.","family":"Jadhav","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8063-8087","authenticated-orcid":false,"given":"Kapil","family":"Rana","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"issue":"1","key":"41_CR1","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/MSP.2021.3120982","volume":"39","author":"F Lago","year":"2021","unstructured":"Lago, F., Pasquini, C., B\u00f6hme, R., Dumont, H., Goffaux, V., Boato, G.: More real than real: a study on human visual perception of synthetic faces [applications corner]. IEEE Signal Process. Mag. 39(1), 109\u2013116 (2021)","journal-title":"IEEE Signal Process. Mag."},{"issue":"07","key":"41_CR2","first-page":"14","volume":"56","author":"N Kshetri","year":"2023","unstructured":"Kshetri, N., DeFranco, J.F., Voas, J.: Is it live, or is it deepfake? Computer 56(07), 14\u201316 (2023)","journal-title":"Computer"},{"key":"41_CR3","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","volume":"64","author":"R Tolosana","year":"2020","unstructured":"Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., Ortega-Garcia, J.: Deepfakes and beyond: a survey of face manipulation and fake detection. Inf. Fusion 64, 131\u2013148 (2020)","journal-title":"Inf. Fusion"},{"key":"41_CR4","doi-asserted-by":"publisher","first-page":"41267","DOI":"10.1109\/ACCESS.2022.3167714","volume":"10","author":"K Rana","year":"2022","unstructured":"Rana, K., Singh, G., Goyal, P.: MSRD-CNN: multi-scale residual deep CNN for general-purpose image manipulation detection. IEEE Access 10, 41267\u201341275 (2022)","journal-title":"IEEE Access"},{"issue":"3s","key":"41_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3510462","volume":"18","author":"G Singh","year":"2022","unstructured":"Singh, G., Goyal, P.: SDCN2: a shallow densely connected CNN for multi-purpose image manipulation detection. ACM Trans. Multimed. Comput. Commun. Appl. 18(3s), 1\u201322 (2022)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"issue":"5","key":"41_CR6","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/JSTSP.2020.3002101","volume":"14","author":"L Verdoliva","year":"2020","unstructured":"Verdoliva, L.: Media forensics and deepfakes: an overview. IEEE J. Sel. Top. Signal Process. 14(5), 910\u2013932 (2020)","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"41_CR7","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.patrec.2023.05.019","volume":"171","author":"K Rana","year":"2023","unstructured":"Rana, K., Singh, G., Goyal, P.: SNRCN2: steganalysis noise residuals based CNN for source social network identification of digital images. Pattern Recogn. Lett. 171, 124\u2013130 (2023)","journal-title":"Pattern Recogn. Lett."},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Marra, F., Saltori, C., Boato, G., Verdoliva, L.: Incremental learning for the detection and classification of GAN-generated images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS), pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/WIFS47025.2019.9035099"},{"key":"41_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103525","volume":"223","author":"TT Nguyen","year":"2022","unstructured":"Nguyen, T.T., et al.: Deep learning for deepfakes creation and detection: a survey. Comput. Vis. Image Underst. 223, 103525 (2022)","journal-title":"Comput. Vis. Image Underst."},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Pasquini, C., Brunetta, C., Vinci, A.F., Conotter, V., Boato, G.: Towards the verification of image integrity in online news. In: 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp.\u00a01\u20136. IEEE (2015)","DOI":"10.1109\/ICMEW.2015.7169801"},{"key":"41_CR11","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Boato, G., Pasquini, C., Stefani, A.L., Verde, S., Miorandi, D.: TrueFace: a dataset for the detection of synthetic face images from social networks. In: 2022 IEEE International Joint Conference on Biometrics (IJCB), pp.\u00a01\u20137. IEEE (2022)","DOI":"10.1109\/IJCB54206.2022.10007988"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"41_CR15","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"41_CR16","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"11","key":"41_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"41_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820\u20132828 (2019)","DOI":"10.1109\/CVPR.2019.00293"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58174-8_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:04:18Z","timestamp":1719911058000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58174-8_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031581731","9783031581748"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58174-8_41","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jammu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","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":"cvip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iitjammu.ac.in\/cvip2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Online CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"461","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":"140","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":"30% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}