{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:30:32Z","timestamp":1785501032875,"version":"3.56.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007741","name":"University of Hyderabad","doi-asserted-by":"publisher","award":["UoH-IoE-RC5-22-012"],"award-info":[{"award-number":["UoH-IoE-RC5-22-012"]}],"id":[{"id":"10.13039\/501100007741","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007741","name":"University of Hyderabad","doi-asserted-by":"publisher","award":["UoH-IoE-RC5-22-012"],"award-info":[{"award-number":["UoH-IoE-RC5-22-012"]}],"id":[{"id":"10.13039\/501100007741","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007741","name":"University of Hyderabad","doi-asserted-by":"publisher","award":["UoH-IoE-RC5-22-012"],"award-info":[{"award-number":["UoH-IoE-RC5-22-012"]}],"id":[{"id":"10.13039\/501100007741","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Multimed Info Retr"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s13735-025-00358-8","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T08:01:33Z","timestamp":1741161693000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Image forgery classification and localization through vision transformers"],"prefix":"10.1007","volume":"14","author":[{"given":"Digambar","family":"Pawar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghavendra","family":"Gowda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna","family":"Chandra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"358_CR1","unstructured":"Amiri E, Mosallanejad A, Sheikhahmadi A et\u00a0al Copy-move forgery detection using eoa, dwt and dct. Pamukkale Univ J Eng Sci, 1000(1000):0\u20130"},{"issue":"18","key":"358_CR2","doi-asserted-by":"publisher","first-page":"7913","DOI":"10.3390\/s23187913","volume":"23","author":"AA Asiri","year":"2023","unstructured":"Asiri AA, Shaf A, Ali T, Pasha MA, Aamir M, Irfan M, Alqahtani S, Alghamdi AJ, Alghamdi AH, Alshamrani AFA (2023) Advancing brain tumor classification through fine-tuned vision transformers: a comparative study of pre-trained models. Sensors 23(18):7913","journal-title":"Sensors"},{"key":"358_CR3","doi-asserted-by":"crossref","unstructured":"Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1053\u20131056. IEEE","DOI":"10.1109\/ICASSP.2009.4959768"},{"issue":"1","key":"358_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TEVC.2022.3220747","volume":"27","author":"Y Bi","year":"2022","unstructured":"Bi Y, Xue B, Mesejo P, Cagnoni S, Zhang M (2022) A survey on evolutionary computation for computer vision and image analysis: past, present, and future trends. IEEE Trans Evolutionary Comput 27(1):5\u201325","journal-title":"IEEE Trans Evolutionary Comput"},{"key":"358_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2020.102967","volume":"73","author":"B Chen","year":"2020","unstructured":"Chen B, Qi X, Zhou Y, Yang G, Zheng Y, Xiao B (2020) Image splicing localization using residual image and residual-based fully convolutional network. J Vis Commun Image Represent 73:102967","journal-title":"J Vis Commun Image Represent"},{"key":"358_CR6","doi-asserted-by":"crossref","unstructured":"Chen CF (Richard), Fan Q, Panda R (2021) Crossvit: Cross-attention multi-scale vision transformer for image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 357\u2013366","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"358_CR7","doi-asserted-by":"crossref","unstructured":"Deng L, Peng J, Deng W, Liu K, Cao Z, Wang W (2022) A dual-stream input faster-cnn model for image forgery detection. In: International Conference on Mobile Networks and Management, pp. 105\u2013115. Springer","DOI":"10.1007\/978-3-031-32443-7_7"},{"key":"358_CR8","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.neucom.2022.09.060","volume":"512","author":"II Ganapathi","year":"2022","unstructured":"Ganapathi II, Javed S, Ali SS, Mahmood A, Vu N-S, Werghi N (2022) Learning to localize image forgery using end-to-end attention network. Neurocomputing 512:25\u201339","journal-title":"Neurocomputing"},{"issue":"3","key":"358_CR9","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"M-H Guo","year":"2022","unstructured":"Guo M-H, Xu T-X, Liu J-J, Liu Z-N, Jiang P-T, Mu T-J, Zhang S-H, Martin RR, Cheng M-M, Hu S-M (2022) Attention mechanisms in computer vision: a survey. Comput Vis Media 8(3):331\u2013368","journal-title":"Comput Vis Media"},{"key":"358_CR10","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.eswa.2017.11.028","volume":"95","author":"D Han","year":"2018","unstructured":"Han D, Liu Q, Fan W (2018) A new image classification method using cnn transfer learning and web data augmentation. Expert Syst Appl 95:43\u201356","journal-title":"Expert Syst Appl"},{"issue":"1","key":"358_CR11","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","volume":"45","author":"K Han","year":"2023","unstructured":"Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y, Yang Z, Zhang Y, Tao D (2023) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45(1):87\u2013110","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"358_CR12","doi-asserted-by":"crossref","unstructured":"He Y, Li Y, Chen C, Li X (2023) Image copy-move forgery detection via deep cross-scale patchmatch. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pages 2327\u20132332. IEEE","DOI":"10.1109\/ICME55011.2023.00397"},{"key":"358_CR13","doi-asserted-by":"crossref","unstructured":"Kadam KD, Ahirrao S, Kotecha K et\u00a0al (2022) Efficient approach towards detection and identification of copy move and image splicing forgeries using mask r-cnn with mobilenet v1. Comput Intell Neurosci","DOI":"10.1155\/2022\/6845326"},{"key":"358_CR14","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"358_CR15","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg Alexander\u00a0C, Lo WY et\u00a0al (2023) Segment anything. arXiv preprint arXiv:2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"358_CR16","doi-asserted-by":"crossref","unstructured":"Lai Y, Luo Z, Yu Z (2023) Detect any deepfakes: Segment anything meets face forgery detection and localization. In: Chinese Conference on Biometric Recognition, pp. 180\u2013190. Springer","DOI":"10.1007\/978-981-99-8565-4_18"},{"key":"358_CR17","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pp. 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"358_CR18","doi-asserted-by":"crossref","unstructured":"Mehrjardi FZ, Latif AM, Zarchi MS, Sheikhpour R (2023) A survey on deep learning-based image forgery detection. Pattern Recognition, pp. 109778","DOI":"10.1016\/j.patcog.2023.109778"},{"key":"358_CR19","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), pp. 565\u2013571. IEEE","DOI":"10.1109\/3DV.2016.79"},{"issue":"03","key":"358_CR20","doi-asserted-by":"publisher","first-page":"2452006","DOI":"10.1142\/S0218001424520062","volume":"38","author":"G Patil","year":"2024","unstructured":"Patil G, Palaiahnakote S, Gornale SS, Lopresti DP (2024) Altered handwritten text detection in document images using deep learning. Int J Pattern Recognit Artific Intell 38(03):2452006","journal-title":"Int J Pattern Recognit Artific Intell"},{"issue":"14","key":"358_CR21","doi-asserted-by":"publisher","first-page":"20925","DOI":"10.1007\/s11042-022-14242-8","volume":"82","author":"G Patil","year":"2023","unstructured":"Patil G, Shivakumara P, Gornale SS, Pal U, Blumenstein M (2023) A new robust approach for altered handwritten text detection. Multimed Tools Appl 82(14):20925\u201320949","journal-title":"Multimed Tools Appl"},{"key":"358_CR22","doi-asserted-by":"crossref","unstructured":"Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia & security, pp 51\u201362","DOI":"10.1145\/1288869.1288878"},{"key":"358_CR23","doi-asserted-by":"crossref","unstructured":"Tan Y, Li Y, Zeng L, Ye J, Li X, et\u00a0al (2023) Multi-scale target-aware framework for constrained image splicing detection and localization. arXiv preprint arXiv:2308.09357","DOI":"10.1145\/3581783.3613763"},{"key":"358_CR24","doi-asserted-by":"crossref","unstructured":"Taneja N, Bramhe VS, Bhardwaj D, Taneja A (2023) Understanding digital image anti-forensics: an analytical review. Multimedia Tools and Applications, pp. 1\u201322","DOI":"10.1007\/s11042-023-15866-0"},{"issue":"2","key":"358_CR25","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1111\/1556-4029.15210","volume":"68","author":"S Tyagi","year":"2023","unstructured":"Tyagi S, Yadav D (2023) Forensicnet: modern convolutional neural network-based image forgery detection network. J Forensic Sci 68(2):461\u2013469","journal-title":"J Forensic Sci"},{"key":"358_CR26","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ins.2019.09.038","volume":"511","author":"B Xiao","year":"2020","unstructured":"Xiao B, Wei Y, Bi X, Li W, Ma J (2020) Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf Sci 511:172\u2013191","journal-title":"Inf Sci"},{"key":"358_CR27","doi-asserted-by":"crossref","unstructured":"Xu Y, Muhammad I, Aiqing F, Jiangbin Z (2023) Multi-scale attention network for detection and localization of image splicing forgery. IEEE Trans Instrum Measur","DOI":"10.1109\/TIM.2023.3300434"},{"key":"358_CR28","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1007\/s11042-016-4289-y","volume":"77","author":"B Yang","year":"2018","unstructured":"Yang B, Sun X, Guo H, Xia Z, Chen X (2018) A copy-move forgery detection method based on cmfd-sift. Multimed Tools Appl 77:837\u2013855","journal-title":"Multimed Tools Appl"},{"key":"358_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2023.117045","volume":"119","author":"J Zhang","year":"2023","unstructured":"Zhang J, Wang H, He P (2023) Dual-branch multi-scale densely connected network for image splicing detection and localization. Signal Process Image Commun 119:117045","journal-title":"Signal Process Image Commun"}],"container-title":["International Journal of Multimedia Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-025-00358-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13735-025-00358-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-025-00358-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T07:33:02Z","timestamp":1742196782000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13735-025-00358-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["358"],"URL":"https:\/\/doi.org\/10.1007\/s13735-025-00358-8","relation":{},"ISSN":["2192-6611","2192-662X"],"issn-type":[{"value":"2192-6611","type":"print"},{"value":"2192-662X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"10 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There are no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"8"}}