{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:20:03Z","timestamp":1776979203022,"version":"3.51.4"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"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":["SIViP"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11760-026-05279-5","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:14:45Z","timestamp":1774948485000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Facial Deepfake Detection Framework Using Swin V2 Vision Transformers and Multi-Scale Textural Descriptors"],"prefix":"10.1007","volume":"20","author":[{"given":"Shivaprakash S","family":"J","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akshat","family":"Chauhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sabireen","family":"H","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul","family":"Quadir Md","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"5279_CR1","doi-asserted-by":"publisher","first-page":"25494","DOI":"10.1109\/ACCESS.2022.3154404","volume":"10","author":"MS Rana","year":"2022","unstructured":"Rana, M.S., Nobi, M.N., Murali, B., Sung, A.H.: Deepfake detection: A systematic literature review. IEEE access 10, 25494\u201325513 (2022)","journal-title":"IEEE access"},{"issue":"1","key":"5279_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3425780","volume":"54","author":"Y Mirsky","year":"2021","unstructured":"Mirsky, Y., Lee, W.: The creation and detection of deepfakes: A survey. ACM computing surveys (CSUR) 54(1), 1\u201341 (2021)","journal-title":"ACM computing surveys (CSUR)"},{"key":"5279_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2022.103525","volume":"223","author":"TT Nguyen","year":"2022","unstructured":"Nguyen, T.T., Nguyen, Q.V.H., Nguyen, D.T., Nguyen, D.T., Huynh-The, T., Nahavandi, S., Nguyen, T.T., Pham, Q.-V., Nguyen, C.M.: Deep learning for deepfakes creation and detection: A survey. Comput. Vis. Image Underst. 223, 103525 (2022)","journal-title":"Comput. Vis. Image Underst."},{"key":"5279_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128571","volume":"292","author":"W Ahmad","year":"2025","unstructured":"Ahmad, W., Peng, Y.-T., Chang, Y.-H.: Fame: a lightweight spatio-temporal network for model attribution of face-swap deepfakes. Expert Syst. Appl. 292, 128571 (2025)","journal-title":"Expert Syst. Appl."},{"key":"5279_CR5","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"5279_CR6","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1109\/LSP.2022.3193590","volume":"29","author":"Y Yuan","year":"2022","unstructured":"Yuan, Y., Fu, X., Wang, G., Li, Q., Li, X.: Forgery-domain-supervised deepfake detection with non-negative constraint. IEEE Signal Process. Lett. 29, 2512\u20132516 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"5279_CR7","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/ACCESS.2022.3232290","volume":"11","author":"V-N Tran","year":"2022","unstructured":"Tran, V.-N., Kwon, S.-G., Lee, S.-H., Le, H.-S., Kwon, K.-R.: Generalization of forgery detection with meta deepfake detection model. IEEE Access 11, 535\u2013546 (2022)","journal-title":"IEEE Access"},{"issue":"3","key":"5279_CR8","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TBIOM.2022.3143404","volume":"4","author":"P Korshunov","year":"2022","unstructured":"Korshunov, P., Marcel, S.: Improving generalization of deepfake detection with data farming and few-shot learning. IEEE Transactions on Biometrics, Behavior, and Identity Science 4(3), 386\u2013397 (2022)","journal-title":"IEEE Transactions on Biometrics, Behavior, and Identity Science"},{"issue":"2","key":"5279_CR9","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TCSVT.2023.3289147","volume":"34","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Wang, L., Yang, W., Yang, G., Li, K.: Ldfnet: Lightweight dynamic fusion network for face forgery detection by integrating local artifacts and global texture information. IEEE Trans. Circuits Syst. Video Technol. 34(2), 1255\u20131265 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5279_CR10","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., Lu, W., Gao, X.: 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":"16","key":"5279_CR11","doi-asserted-by":"publisher","first-page":"49013","DOI":"10.1007\/s11042-023-17586-x","volume":"83","author":"NU Huda","year":"2024","unstructured":"Huda, N.U., Javed, A., Maswadi, K., Alhazmi, A., Ashraf, R.: Fake-checker: A fusion of texture features and deep learning for deepfakes detection. Multimedia Tools and Applications 83(16), 49013\u201349037 (2024)","journal-title":"Multimedia Tools and Applications"},{"issue":"10","key":"5279_CR12","doi-asserted-by":"publisher","first-page":"4746","DOI":"10.1007\/s11263-024-02116-5","volume":"132","author":"Y Wang","year":"2024","unstructured":"Wang, Y., Chen, C., Zhang, N., Hu, X.: Watcher: Wavelet-guided texture-content hierarchical relation learning for deepfake detection. Int. J. Comput. Vision 132(10), 4746\u20134767 (2024)","journal-title":"Int. J. Comput. Vision"},{"key":"5279_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cheng, K., Sovernigo, G., Lin, X.: A heterogeneous feature ensemble learning based deepfake detection method. In: ICC 2022-IEEE International Conference on Communications, pp. 2084\u20132089 (2022). IEEE","DOI":"10.1109\/ICC45855.2022.9838630"},{"issue":"9","key":"5279_CR14","doi-asserted-by":"publisher","first-page":"8972","DOI":"10.1109\/TCSVT.2024.3390945","volume":"34","author":"D Zhang","year":"2024","unstructured":"Zhang, D., Chen, J., Liao, X., Li, F., Chen, J., Yang, G.: Face forgery detection via multi-feature fusion and local enhancement. IEEE Trans. Circuits Syst. Video Technol. 34(9), 8972\u20138977 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5279_CR15","doi-asserted-by":"crossref","unstructured":"Kong, C., Luo, A., Bao, P., Yu, Y., Li, H., Zheng, Z., Wang, S., Kot, A.C.: Moe-ffd: Mixture of experts for generalized and parameter-efficient face forgery detection. IEEE Transactions on Dependable and Secure Computing (2025)","DOI":"10.1109\/TDSC.2025.3604443"},{"key":"5279_CR16","doi-asserted-by":"crossref","unstructured":"Kong, C., Chen, B., Li, H., Wang, S., Rocha, A., Kwong, S.: Detect and locate: Exposing face manipulation by semantic-and noise-level telltales. IEEE Trans. Inf. Forensics Secur. 17, 1741\u20131756 (2022)","DOI":"10.1109\/TIFS.2022.3169921"},{"key":"5279_CR17","doi-asserted-by":"crossref","unstructured":"Kong, C., Luo, A., Wang, S., Li, H., Rocha, A., Kot, A.C.: Pixel-inconsistency modeling for image manipulation localization. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)","DOI":"10.1109\/TPAMI.2025.3541028"},{"key":"5279_CR18","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1109\/TIFS.2023.3332218","volume":"19","author":"A Luo","year":"2023","unstructured":"Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. IEEE Trans. Inf. Forensics Secur. 19, 1168\u20131182 (2023)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"5","key":"5279_CR19","doi-asserted-by":"publisher","first-page":"4116","DOI":"10.1109\/TCSVT.2024.3522091","volume":"35","author":"A Luo","year":"2024","unstructured":"Luo, A., Cai, R., Kong, C., Ju, Y., Kang, X., Huang, J., Kot, A.C.: Forgery-aware adaptive learning with vision transformer for generalized face forgery detection. IEEE Trans. Circuits Syst. Video Technol. 35(5), 4116\u20134129 (2024)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"9","key":"5279_CR20","doi-asserted-by":"publisher","first-page":"4462","DOI":"10.1109\/TCSVT.2023.3281448","volume":"33","author":"Y Yu","year":"2023","unstructured":"Yu, Y., Ni, R., Zhao, Y., Yang, S., Xia, F., Jiang, N., Zhao, G.: Msvt: Multiple spatiotemporal views transformer for deepfake video detection. IEEE Trans. Circuits Syst. Video Technol. 33(9), 4462\u20134471 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"4","key":"5279_CR21","doi-asserted-by":"publisher","first-page":"2803","DOI":"10.1109\/TCSVT.2023.3312738","volume":"34","author":"X Liu","year":"2023","unstructured":"Liu, X., Yu, Y., Li, X., Zhao, Y.: Mcl: multimodal contrastive learning for deepfake detection. IEEE Trans. Circuits Syst. Video Technol. 34(4), 2803\u20132813 (2023)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"5279_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3207\u20133216 (2020)","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"5279_CR23","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nie\u00dfner, M.: 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"},{"issue":"10","key":"5279_CR24","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":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05279-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05279-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05279-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:32:27Z","timestamp":1776976347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05279-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["5279"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05279-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"assertion":[{"value":"19 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The codebase is currently being extended in a follow-up study. After completion, it will be released on GitHub.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"233"}}