{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T17:18:16Z","timestamp":1769361496966,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00521-025-11461-6","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T19:19:07Z","timestamp":1752175147000},"page":"20365-20381","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Detecting image forgery: a deep learning framework with feature pyramid integration for inpainting detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Soha Ahmed Ehssan","family":"Aly","sequence":"first","affiliation":[]},{"given":"Omar","family":"Emara","sequence":"additional","affiliation":[]},{"given":"Hamza","family":"Mahmoud","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3028-6500","authenticated-orcid":false,"given":"Saddam","family":"Bekhet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"issue":"1","key":"11461_CR1","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1080\/02564602.2020.1782274","volume":"38","author":"X Wang","year":"2021","unstructured":"Wang X, Niu S, Wang H (2021) Image inpainting detection based on multi-task deep learning network. IETE Techn Rev 38(1):149\u2013157","journal-title":"IETE Techn Rev"},{"key":"11461_CR2","unstructured":"Adobe Inc.: Adobe Photoshop (2022). https:\/\/www.adobe.com\/products\/photoshop.html"},{"key":"11461_CR3","unstructured":"Pixlr Pte Ltd.: Pixlr (2022). https:\/\/www.pixlr.com\/"},{"issue":"5","key":"11461_CR4","doi-asserted-by":"publisher","first-page":"6259","DOI":"10.1007\/s11042-021-11733-y","volume":"81","author":"T Zhang","year":"2022","unstructured":"Zhang T (2022) Deepfake generation and detection, a survey. Multim Tools Appl 81(5):6259\u20136276","journal-title":"Multim Tools Appl"},{"key":"11461_CR5","doi-asserted-by":"publisher","first-page":"1400024","DOI":"10.3389\/fdata.2024.1400024","volume":"7","author":"E Altuncu","year":"2024","unstructured":"Altuncu E, Franqueira VN, Li S (2024) Deepfake: definitions, performance metrics and standards, datasets, and a meta-review. Front Big Data 7:1400024","journal-title":"Front Big Data"},{"key":"11461_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109046","volume":"134","author":"H Xiang","year":"2023","unstructured":"Xiang H, Zou Q, Nawaz MA, Huang X, Zhang F, Yu H (2023) Deep learning for image inpainting: a survey. Patt Recognit 134:109046","journal-title":"Patt Recognit"},{"issue":"20","key":"11461_CR7","doi-asserted-by":"publisher","first-page":"11189","DOI":"10.3390\/app132011189","volume":"13","author":"Z Xu","year":"2023","unstructured":"Xu Z, Zhang X, Chen W, Yao M, Liu J, Xu T, Wang Z (2023) A review of image inpainting methods based on deep learning. Appl Sci 13(20):11189","journal-title":"Appl Sci"},{"issue":"2","key":"11461_CR8","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3390\/jimaging10020042","volume":"10","author":"A-A Barglazan","year":"2024","unstructured":"Barglazan A-A, Brad R, Constantinescu C (2024) Image inpainting forgery detection: a review. Journal of Imaging 10(2):42","journal-title":"Journal of Imaging"},{"issue":"9","key":"11461_CR9","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TIP.2004.833105","volume":"13","author":"A Criminisi","year":"2004","unstructured":"Criminisi A, P\u00e9rez P, Toyama K (2004) Region filling and object removal by exemplar-based image inpainting. IEEE Trans Image Process 13(9):1200\u20131212","journal-title":"IEEE Trans Image Process"},{"key":"11461_CR10","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.inffus.2022.08.033","volume":"90","author":"X Zhang","year":"2023","unstructured":"Zhang X, Zhai D, Li T, Zhou Y, Lin Y (2023) Image inpainting based on deep learning: a review. Inf Fusion 90:74\u201394","journal-title":"Inf Fusion"},{"key":"11461_CR11","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.ins.2021.04.042","volume":"572","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Ding F, Kwong S, Zhu G (2021) Feature pyramid network for diffusion-based image inpainting detection. Inf Sci 572:29\u201342","journal-title":"Inf Sci"},{"issue":"3","key":"11461_CR12","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1109\/TCSVT.2021.3075039","volume":"32","author":"H Wu","year":"2021","unstructured":"Wu H, Zhou J (2021) Iid-net: Image inpainting detection network via neural architecture search and attention. IEEE Trans Circuit Syst Video Technol 32(3):1172\u20131185","journal-title":"IEEE Trans Circuit Syst Video Technol"},{"key":"11461_CR13","doi-asserted-by":"crossref","unstructured":"Liu L, Ye C, Wang K, Liu Z, Mao Y, Ma L (2023) Image inpainting algorithm based on image decomposition model and patch global structural information. In: 2022 2nd conference on high performance computing and communication engineering (HPCCE 2022), vol. 12605, pp. 235\u2013241 . SPIE","DOI":"10.1117\/12.2673237"},{"issue":"1","key":"11461_CR14","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.imavis.2012.09.002","volume":"31","author":"I-C Chang","year":"2013","unstructured":"Chang I-C, Yu JC, Chang C-C (2013) A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image Vision Comput 31(1):57\u201371","journal-title":"Image Vision Comput"},{"key":"11461_CR15","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jvcir.2015.03.004","volume":"30","author":"Z Liang","year":"2015","unstructured":"Liang Z, Yang G, Ding X, Li L (2015) An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. J Vis Commun Image Represent 30:75\u201385","journal-title":"J Vis Commun Image Represent"},{"issue":"12","key":"11461_CR16","doi-asserted-by":"publisher","first-page":"3050","DOI":"10.1109\/TIFS.2017.2730822","volume":"12","author":"H Li","year":"2017","unstructured":"Li H, Luo W, Huang J (2017) Localization of diffusion-based inpainting in digital images. IEEE Trans Inf Forens Secur 12(12):3050\u20133064","journal-title":"IEEE Trans Inf Forens Secur"},{"issue":"3","key":"11461_CR17","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1109\/TCSVT.2023.3299278","volume":"34","author":"Y Li","year":"2023","unstructured":"Li Y, Hu L, Dong L, Wu H, Tian J, Zhou J, Li X (2023) Transformer-based image inpainting detection via label decoupling and constrained adversarial training. IEEE Trans Circuit Syst Video Technol 34(3):1857\u20131872","journal-title":"IEEE Trans Circuit Syst Video Technol"},{"key":"11461_CR18","doi-asserted-by":"crossref","unstructured":"Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6721\u20136729 (2017)","DOI":"10.1109\/CVPR.2017.434"},{"key":"11461_CR19","volume":"2020","author":"X Wang","year":"2020","unstructured":"Wang X, Wang H, Niu S (2020) An intelligent forensics approach for detecting patch-based image inpainting. Mathem Probl Eng 2020:9288989","journal-title":"Mathem Probl Eng"},{"key":"11461_CR20","doi-asserted-by":"crossref","unstructured":"Yu J, Lin Z, Yang J, Shen X, Lu X, Huang TS (2019) Free-form image inpainting with gated convolution. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp. 4471\u20134480","DOI":"10.1109\/ICCV.2019.00457"},{"issue":"6","key":"11461_CR21","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.1007\/s00530-023-01184-w","volume":"29","author":"F Xiao","year":"2023","unstructured":"Xiao F, Zhang Z, Yao Y (2023) Ctnet: hybrid architecture based on cnn and transformer for image inpainting detection. Multim Syst 29(6):3819\u20133832","journal-title":"Multim Syst"},{"key":"11461_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123331","volume":"247","author":"Y Yao","year":"2024","unstructured":"Yao Y, Han T, Gao X, Ren Y, Meng W (2024) Deep video inpainting detection and localization based on convnext dual-stream network. Expert Syst Appl 247:123331","journal-title":"Expert Syst Appl"},{"key":"11461_CR23","volume":"84","author":"X Ding","year":"2024","unstructured":"Ding X, Deng Y, Zhao Y, Zhu W (2024) Aftlnet: an efficient adaptive forgery traces learning network for deep image inpainting localization. J Inf Secur Appl 84:103825","journal-title":"J Inf Secur Appl"},{"issue":"2","key":"11461_CR24","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.11591\/ijece.v12i2.pp2066-2078","volume":"12","author":"S Bekhet","year":"2022","unstructured":"Bekhet S, Alghamdi AM, Taj-Eddin I (2022) Gender recognition from unconstrained selfie images: a convolutional neural network approach. Int J Elect Comput Eng 12(2):2066\u20132078","journal-title":"Int J Elect Comput Eng"},{"issue":"2","key":"11461_CR25","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos N, Vasanthavada N, Baker RL (1988) Design of an image edge detection filter using the sobel operator. IEEE J Solid-State Circuits 23(2):358\u2013367","journal-title":"IEEE J Solid-State Circuits"},{"key":"11461_CR26","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.neucom.2022.06.083","volume":"503","author":"J Jing","year":"2022","unstructured":"Jing J, Liu S, Wang G, Zhang W, Sun C (2022) Recent advances on image edge detection: a comprehensive review. Neurocomputing 503:259\u2013271","journal-title":"Neurocomputing"},{"key":"11461_CR27","doi-asserted-by":"publisher","first-page":"107993","DOI":"10.1109\/ACCESS.2022.3213439","volume":"10","author":"A Humeau-Heurtier","year":"2022","unstructured":"Humeau-Heurtier A (2022) Color texture analysis: a survey. IEEE Access 10:107993\u2013108003","journal-title":"IEEE Access"},{"key":"11461_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2023.104124","volume":"140","author":"S Lan","year":"2023","unstructured":"Lan S, Liao X, Fan H, Hu S, Pan Z (2023) A multi-channel framework based local binary pattern with two novel local feature descriptors for texture classification. Digital Signal Process 140:104124","journal-title":"Digital Signal Process"},{"key":"11461_CR29","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"11461_CR30","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1007\/s11554-017-0700-9","volume":"16","author":"S Bekhet","year":"2019","unstructured":"Bekhet S, Ahmed A (2019) Video similarity detection using fixed-length statistical dominant colour profile (sdcp) signatures. J Real-Time Image Processing 16(6):1999\u20132014","journal-title":"J Real-Time Image Processing"},{"key":"11461_CR31","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.neucom.2022.02.016","volume":"483","author":"L Zhu","year":"2022","unstructured":"Zhu L, Lee F, Cai J, Yu H, Chen Q (2022) An improved feature pyramid network for object detection. Neurocomputing 483:127\u2013139","journal-title":"Neurocomputing"},{"key":"11461_CR32","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision, pp. 740\u2013755 . Springer","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"1","key":"11461_CR33","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1049\/bme2.12004","volume":"10","author":"S Bekhet","year":"2021","unstructured":"Bekhet S, Alahmer H (2021) A robust deep learning approach for glasses detection in non-standard facial images. IET Biometrics 10(1):74\u201386","journal-title":"IET Biometrics"},{"key":"11461_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, Wang H, Niu S (2019) An image forensic method for ai inpainting using faster r-cnn. In: artificial intelligence and security: 5th international conference, ICAIS 2019, New York, NY, USA, July 26\u201328, 2019, Proceedings, Part III 5, pp. 476\u2013487 . Springer","DOI":"10.1007\/978-3-030-24271-8_43"},{"key":"11461_CR35","first-page":"90","volume":"67","author":"X Zhu","year":"2018","unstructured":"Zhu X, Qian Y, Zhao X, Sun B, Sun Y (2018) A deep learning approach to patch-based image inpainting forensics. Signal Process: Image Commun 67:90\u201399","journal-title":"Signal Process: Image Commun"},{"key":"11461_CR36","first-page":"472","volume":"5307","author":"G Schaefer","year":"2003","unstructured":"Schaefer G, Stich M (2003) Ucid: an uncompressed color image database. Storage Retr Methods Appl Multim 5307:472\u2013480","journal-title":"Storage Retr Methods Appl Multim"},{"key":"11461_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yin Z, Li Y, Yin G, Yan J, Shao J, Liu Z (2020) Celeba-spoof: Large-scale face anti-spoofing dataset with rich annotations. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII 16, pp. 70\u201385 . Springer","DOI":"10.1007\/978-3-030-58610-2_5"},{"issue":"4","key":"11461_CR38","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s10723-023-09684-9","volume":"21","author":"SMA Naqvi","year":"2023","unstructured":"Naqvi SMA, Shabaz M, Khan MA, Hassan SI (2023) Adversarial attacks on visual objects using the fast gradient sign method. J Grid Computing 21(4):52","journal-title":"J Grid Computing"},{"key":"11461_CR39","first-page":"84","volume":"13","author":"R Sapkota","year":"2024","unstructured":"Sapkota R, Ahmed D, Karkee M (2024) Comparing yolov8 and mask r-cnn for instance segmentation in complex orchard environments. Artif Intell Agric 13:84\u201399","journal-title":"Artif Intell Agric"},{"key":"11461_CR40","doi-asserted-by":"crossref","unstructured":"Arora T, Soni R (2021) A review of techniques to detect the gan-generated fake images. Generative Adversarial Networks for Image-to-Image Translation, 125\u2013159","DOI":"10.1016\/B978-0-12-823519-5.00004-X"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11461-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11461-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11461-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T04:48:28Z","timestamp":1757220508000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11461-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":40,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["11461"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11461-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,10]]},"assertion":[{"value":"24 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 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":"No Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}