{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:08:25Z","timestamp":1764842905231,"version":"3.41.0"},"reference-count":136,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19843-z","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T08:01:50Z","timestamp":1721721710000},"page":"20721-20756","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A systematic literature review on image splicing detection and localization using emerging technologies"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6424-3007","authenticated-orcid":false,"given":"Chithra Raj","family":"N.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2608-6821","authenticated-orcid":false,"given":"Maitreyee","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6903-3722","authenticated-orcid":false,"given":"Jagriti","family":"Saini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"19843_CR1","unstructured":"Grobler M (2010) Digital Forensic Standards: international progress. In: proceedings of the South African Information Security Multi-Conference(SAISMC 2010). pp 261\u201371"},{"key":"19843_CR2","doi-asserted-by":"publisher","unstructured":"Moore GE (2006) Cramming more components onto integrated circuits, Reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff. IEEE Solid-State Circuits Soc Newsletter 11:33\u201335. https:\/\/doi.org\/10.1109\/N-SSC.2006.4785860","DOI":"10.1109\/N-SSC.2006.4785860"},{"key":"19843_CR3","unstructured":"May T (2023) The best photo-editing software in August 2023. In: creative bloq. https:\/\/www.creativebloq.com\/features\/photo-editing-software"},{"key":"19843_CR4","doi-asserted-by":"publisher","first-page":"1841","DOI":"10.1109\/TIFS.2012.2218597","volume":"7","author":"V Christlein","year":"2012","unstructured":"Christlein V, Riess C, Jordan J et al (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7:1841\u20131854. https:\/\/doi.org\/10.1109\/TIFS.2012.2218597","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR5","unstructured":"Ng T-T, Chang S-F, Sun Q (2004) Blind detection of photomontage using higher order statistics. In: 2004 IEEE International Symposium on Circuits and Systems (ISCAS). p V\u2013V"},{"key":"19843_CR6","unstructured":"Columbia University (2004) Columbia image splicing detection evaluation dataset. https:\/\/www.ee.columbia.edu\/ln\/dvmm\/downloads\/AuthSplicedDataSet\/AuthSplicedDataSet.htm"},{"key":"19843_CR7","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.1109\/TIFS.2016.2561898","volume":"11","author":"A Bharati","year":"2016","unstructured":"Bharati A, Singh R, Vatsa M, Bowyer KW (2016) Detecting facial retouching using supervised deep learning. IEEE Trans Inf Forensics Secur 11:1903\u20131913. https:\/\/doi.org\/10.1109\/TIFS.2016.2561898","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR8","doi-asserted-by":"publisher","unstructured":"Nabi ST, Kumar M, Singh P et al (2022) A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions. Multimedia Systems 28:939\u2013992. https:\/\/doi.org\/10.1007\/s00530-021-00873-8","DOI":"10.1007\/s00530-021-00873-8"},{"key":"19843_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102092","author":"E Nowroozi","year":"2020","unstructured":"Nowroozi E, Dehghantanha A, Parizi R, Choo K-KR (2020) A survey of machine learning techniques in adversarial image forensics. Comput Secur. https:\/\/doi.org\/10.1016\/j.cose.2020.102092","journal-title":"Comput Secur"},{"key":"19843_CR10","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/jimaging6030009","volume":"6","author":"P Yang","year":"2020","unstructured":"Yang P, Baracchi D, Ni R et al (2020) A survey of deep learning-based source image forensics. J Imaging 6:9. https:\/\/doi.org\/10.3390\/jimaging6030009","journal-title":"J Imaging"},{"key":"19843_CR11","doi-asserted-by":"publisher","first-page":"106685","DOI":"10.1016\/j.compeleceng.2020.106685","volume":"85","author":"WD Ferreira","year":"2020","unstructured":"Ferreira WD, Ferreira CBR, da Cruz JG, Soares F (2020) A review of digital image forensics. Comput Electr Eng 85:106685. https:\/\/doi.org\/10.1016\/j.compeleceng.2020.106685","journal-title":"Comput Electr Eng"},{"key":"19843_CR12","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1080\/00450618.2018.1424241","volume":"51","author":"S Walia","year":"2019","unstructured":"Walia S, Kumar K (2019) Digital image forgery detection: a systematic scrutiny. Aust J Forensic Sci 51:488\u2013526. https:\/\/doi.org\/10.1080\/00450618.2018.1424241","journal-title":"Aust J Forensic Sci"},{"key":"19843_CR13","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1007\/s10462-021-10046-8","volume":"55","author":"S Gupta","year":"2022","unstructured":"Gupta S, Mohan N, Kaushal P (2022) Passive image forensics using universal techniques: a review. Artif Intell Rev 55:1629\u20131679. https:\/\/doi.org\/10.1007\/s10462-021-10046-8","journal-title":"Artif Intell Rev"},{"key":"19843_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2018.12.022","author":"L Zheng","year":"2018","unstructured":"Zheng L, Zhang Y, Thing V (2018) A Survey on image tampering and its detection in real-world photos. J Vis Commun Image Represent. https:\/\/doi.org\/10.1016\/j.jvcir.2018.12.022","journal-title":"J Vis Commun Image Represent"},{"key":"19843_CR15","unstructured":"Sekhar C, Sankar TN (2016) Review on image splicing forgery detection. Int J Comput Sci Inform Secur (IJCSIS) 14(11):471. https:\/\/sites.google.com\/site\/ijcsis\/"},{"key":"19843_CR16","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"19843_CR17","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1109\/JPROC.2008.925411","volume":"96","author":"JR Powell","year":"2008","unstructured":"Powell JR (2008) The quantum limit to moore\u2019s law. Proc IEEE 96:1247\u20131248. https:\/\/doi.org\/10.1109\/JPROC.2008.925411","journal-title":"Proc IEEE"},{"key":"19843_CR18","doi-asserted-by":"publisher","first-page":"2050024","DOI":"10.1142\/S0219749920500240","volume":"18","author":"A Gokhale","year":"2020","unstructured":"Gokhale A, Pande MB, Pramod D (2020) Implementation of a quantum transfer learning approach to image splicing detection. Int J Quantum Inform World Sci Publishing Co 18:2050024. https:\/\/doi.org\/10.1142\/S0219749920500240","journal-title":"Int J Quantum Inform World Sci Publishing Co"},{"key":"19843_CR19","doi-asserted-by":"publisher","unstructured":"Kaur A, Kanwal N, Kaur L (2020) A comparative review of various techniques for image splicing detection and localization. In: Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Springer Singapore. https:\/\/doi.org\/10.1007\/978-981-15-3369-3_11, pp 139\u2013156","DOI":"10.1007\/978-981-15-3369-3_11"},{"key":"19843_CR20","doi-asserted-by":"publisher","unstructured":"Wang J, Li Y (2019) Splicing image and its localization: a survey. J Inform Hiding Privacy Protect Tech Sci Press 1:77. https:\/\/doi.org\/10.32604\/jihpp.2019.07186","DOI":"10.32604\/jihpp.2019.07186"},{"key":"19843_CR21","doi-asserted-by":"crossref","unstructured":"Meena KB, Tyagi V (2021) Image splicing forgery detection techniques: a review. In: Advances in computing and data sciences: 5th international conference, ICACDS 2021, Nashik, India, vol 1441. Springer International Publishing, pp 364\u2013388","DOI":"10.1007\/978-3-030-88244-0_35"},{"key":"19843_CR22","doi-asserted-by":"publisher","unstructured":"Zanardelli M, Guerrini F, Leonardi R, Adami N (2022) Image forgery detection: a survey of recent deep-learning approaches | multimedia tools and applications. In: Springer. https:\/\/link.springer.com\/article\/https:\/\/doi.org\/10.1007\/s11042-022-13797-w. Accessed 28 Sep 2023","DOI":"10.1007\/s11042-022-13797-w"},{"key":"19843_CR23","unstructured":"Bhel H (2022) How AI will transform digital forensics in 2022 and beyond - ETCIO SEA. In: Published on 2 May 2022. https:\/\/ciosea.economictimes.indiatimes.com\/blog\/how-ai-will-transform-digital-forensics-in-2022-and-beyond\/91141155. Accessed\u00a012\/2\/2022"},{"key":"19843_CR24","unstructured":"Bhuiyan J (2022) \u2018A catastrophic failure\u2019: computer scientist Hany Farid on why violent videos circulate on the internet. The Guardian (online) https:\/\/www.theguardian.com\/media\/2022\/may\/19\/hany-farid-violent-videos-hashing-internet-interview. Accessed\u00a05\/3\/2023"},{"key":"19843_CR25","unstructured":"National Institute of Justice (2022) Digital evidence and forensics. In: National institute of justice. https:\/\/nij.ojp.gov\/digital-evidence-and-forensics. Accessed\u00a05\/3\/2023"},{"key":"19843_CR26","unstructured":"FileTSAR (2016) File Toolkit for Selective Analysis & Reconstruction (FileTSAR) for large scale computer networks. In: National institute of justice. https:\/\/nij.ojp.gov\/funding\/awards\/2016-mu-mu-k091. Accessed\u00a05\/3\/2023"},{"key":"19843_CR27","unstructured":"DeepPatrol (2016) Finding illicit videos for law enforcement. In: National institute of justice. https:\/\/nij.ojp.gov\/funding\/awards\/2016-mu-cx-k015. Accessed\u00a05\/3\/2023"},{"key":"19843_CR28","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1049\/iet-ipr.2017.1120","volume":"12","author":"R Cristin","year":"2018","unstructured":"Cristin R, Ananth JP, Raj VC (2018) Illumination-based texture descriptor and fruitfly support vector neural network for image forgery detection in face images. IET Image Proc 12:1439\u20131449. https:\/\/doi.org\/10.1049\/iet-ipr.2017.1120","journal-title":"IET Image Proc"},{"key":"19843_CR29","doi-asserted-by":"publisher","first-page":"12457","DOI":"10.1007\/s11042-016-3660-3","volume":"76","author":"H Yao","year":"2017","unstructured":"Yao H, Wang S, Zhang X et al (2017) Detecting image splicing based on noise level inconsistency. Multimed Tools Appl 76:12457\u201312479. https:\/\/doi.org\/10.1007\/s11042-016-3660-3","journal-title":"Multimed Tools Appl"},{"key":"19843_CR30","unstructured":"Wikipedia (2022) Image noise. Last edited on 12 July 2022, at 10:39 (UTC). https:\/\/en.wikipedia.org\/w\/index.php?title=Image_noise&oldid=1097727906. Accessed\u00a05\/3\/2023"},{"key":"19843_CR31","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.compeleceng.2017.05.008","volume":"62","author":"S Farooq","year":"2017","unstructured":"Farooq S, Yousaf MH, Hussain F (2017) A generic passive image forgery detection scheme using local binary pattern with rich models. Comput Electr Eng 62:459\u2013472. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.05.008","journal-title":"Comput Electr Eng"},{"key":"19843_CR32","doi-asserted-by":"publisher","first-page":"25851","DOI":"10.1007\/s11042-017-5189-5","volume":"76","author":"MM Isaac","year":"2017","unstructured":"Isaac MM, Wilscy M (2017) Multiscale local gabor phase quantization for image forgery detection. Multimed Tools Appl 76:25851\u201325872. https:\/\/doi.org\/10.1007\/s11042-017-5189-5","journal-title":"Multimed Tools Appl"},{"key":"19843_CR33","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s11760-016-1023-1","volume":"11","author":"OM Fahmy","year":"2017","unstructured":"Fahmy OM (2017) A new Zernike moments based technique for camera identification and forgery detection. SIViP 11:785\u2013792. https:\/\/doi.org\/10.1007\/s11760-016-1023-1","journal-title":"SIViP"},{"key":"19843_CR34","doi-asserted-by":"publisher","first-page":"14535","DOI":"10.1007\/s11042-016-3855-7","volume":"76","author":"E-SM El-Alfy","year":"2017","unstructured":"El-Alfy E-SM, Qureshi MA (2017) Robust content authentication of gray and color images using lbp-dct markov-based features. Multimed Tools Appl 76:14535\u201314556. https:\/\/doi.org\/10.1007\/s11042-016-3855-7","journal-title":"Multimed Tools Appl"},{"key":"19843_CR35","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1049\/iet-ipr.2016.0238","volume":"11","author":"X Shen","year":"2017","unstructured":"Shen X, Shi Z, Chen H (2017) Splicing image forgery detection using textural features based on the grey level co-occurrence matrices. IET Image Proc 11:44\u201353. https:\/\/doi.org\/10.1049\/iet-ipr.2016.0238","journal-title":"IET Image Proc"},{"key":"19843_CR36","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.ijleo.2018.07.021","volume":"172","author":"S Sharma","year":"2018","unstructured":"Sharma S, Ghanekar U (2018) A hybrid technique to discriminate natural images, computer generated graphics images, spliced, copy move tampered images and authentic images by using features and ELM classifier. Optik 172:470\u2013483. https:\/\/doi.org\/10.1016\/j.ijleo.2018.07.021","journal-title":"Optik"},{"key":"19843_CR37","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s00138-018-0911-5","volume":"29","author":"JG Han","year":"2018","unstructured":"Han JG, Park TH, Moon YH, Eom IK (2018) Quantization-based Markov feature extraction method for image splicing detection. Mach Vis Appl 29:543\u2013552. https:\/\/doi.org\/10.1007\/s00138-018-0911-5","journal-title":"Mach Vis Appl"},{"key":"19843_CR38","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.image.2018.04.011","volume":"66","author":"B Liu","year":"2018","unstructured":"Liu B, Pun C-M (2018) Locating splicing forgery by fully convolutional networks and conditional random field. Signal Process: Image Commun 66:103\u2013112. https:\/\/doi.org\/10.1016\/j.image.2018.04.011","journal-title":"Signal Process: Image Commun"},{"key":"19843_CR39","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/MCOM.2018.1700817","volume":"56","author":"A Ghoneim","year":"2018","unstructured":"Ghoneim A, Muhammad G, Amin S, Gupta BB (2018) Medical image forgery detection for smart healthcare. IEEE Commun Mag 56:33\u201337. https:\/\/doi.org\/10.1109\/MCOM.2018.1700817","journal-title":"IEEE Commun Mag"},{"issue":"8","key":"19843_CR40","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1049\/iet-ipr.2017.0683","volume":"12","author":"B Yang","year":"2018","unstructured":"Yang B, Sun X, Cao E, Hu W, Chen X (2018) Convolutional neural network for smooth filtering detection. IET Image Process 12(8):1432\u20131438","journal-title":"IET Image Process"},{"key":"19843_CR41","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.image.2018.07.012","volume":"68","author":"N Zhu","year":"2018","unstructured":"Zhu N, Li Z (2018) Blind image splicing detection via noise level function. Signal Process: Image Commun 68:181\u2013192. https:\/\/doi.org\/10.1016\/j.image.2018.07.012","journal-title":"Signal Process: Image Commun"},{"key":"19843_CR42","doi-asserted-by":"publisher","unstructured":"Mire AV, Dhok SB, Mistry NJ, Porey PD\u00a0(2018) Automated approach for splicing detection using first digit probability distribution features. EURASIP J Image Video Process 18(2018). https:\/\/doi.org\/10.1186\/s13640-018-0257-y","DOI":"10.1186\/s13640-018-0257-y"},{"key":"19843_CR43","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1109\/TIFS.2017.2752728","volume":"13","author":"B Peng","year":"2018","unstructured":"Peng B, Wang W, Dong J, Tan T (2018) Image forensics based on planar contact constraints of 3D objects. IEEE Trans Inf Forensics Secur 13:377\u2013392. https:\/\/doi.org\/10.1109\/TIFS.2017.2752728","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR44","doi-asserted-by":"publisher","first-page":"3372","DOI":"10.3390\/s18103372","volume":"18","author":"EA Armas Vega","year":"2018","unstructured":"Armas Vega EA, Sandoval Orozco AL, Garc\u00eda Villalba LJ, Hernandez-Castro J (2018) Digital images authentication technique based on DWT. DCT and Local Binary Patterns Sens 18:3372. https:\/\/doi.org\/10.3390\/s18103372","journal-title":"DCT and Local Binary Patterns Sens"},{"key":"19843_CR45","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.jvcir.2018.01.010","volume":"51","author":"R Salloum","year":"2018","unstructured":"Salloum R, Ren Y, Jay Kuo C-C (2018) Image splicing Localization using a Multi-task Fully Convolutional Network (MFCN). J Vis Commun Image Represent 51:201\u2013209. https:\/\/doi.org\/10.1016\/j.jvcir.2018.01.010","journal-title":"J Vis Commun Image Represent"},{"key":"19843_CR46","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1049\/iet-ipr.2017.1131","volume":"12","author":"H Sheng","year":"2018","unstructured":"Sheng H, Shen X, Lyu Y et al (2018) Image splicing detection based on Markov features in discrete octonion cosine transform domain. IET Image Proc 12:1815\u20131823. https:\/\/doi.org\/10.1049\/iet-ipr.2017.1131","journal-title":"IET Image Proc"},{"key":"19843_CR47","doi-asserted-by":"publisher","first-page":"4581","DOI":"10.3934\/mbe.2019229","volume":"16","author":"XY Wang","year":"2019","unstructured":"Wang XY, Wang H, Niu SZ, Zhang JW (2019) Detection and localization of image forgeries using improved mask regional convolutional neural network. Math Biosci Eng 16:4581\u20134593. https:\/\/doi.org\/10.3934\/mbe.2019229","journal-title":"Math Biosci Eng"},{"key":"19843_CR48","doi-asserted-by":"publisher","first-page":"11601","DOI":"10.1109\/JSEN.2019.2928480","volume":"19","author":"C Song","year":"2019","unstructured":"Song C, Zeng P, Wang Z et al (2019) Image forgery detection based on motion blur estimated using convolutional neural network. IEEE Sens J 19:11601\u201311611. https:\/\/doi.org\/10.1109\/JSEN.2019.2928480","journal-title":"IEEE Sens J"},{"key":"19843_CR49","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s00138-019-01048-2","volume":"30","author":"K Asghar","year":"2019","unstructured":"Asghar K, Sun X, Rosin PL et al (2019) Edge\u2013texture feature-based image forgery detection with cross-dataset evaluation. Mach Vis Appl 30:1243\u20131262. https:\/\/doi.org\/10.1007\/s00138-019-01048-2","journal-title":"Mach Vis Appl"},{"key":"19843_CR50","doi-asserted-by":"publisher","first-page":"E371","DOI":"10.3390\/e21040371","volume":"21","author":"HA Jalab","year":"2019","unstructured":"Jalab HA, Subramaniam T, Ibrahim RW et al (2019) New texture descriptor based on modified fractional entropy for digital image splicing forgery detection. Entropy (Basel) 21:E371. https:\/\/doi.org\/10.3390\/e21040371","journal-title":"Entropy (Basel)"},{"key":"19843_CR51","doi-asserted-by":"publisher","unstructured":"Zhang J, Li Y, Niu S, et al (2019) Improved fully convolutional network for digital image region forgery detection. Comput Mater Continua 58:287\u2013303. https:\/\/doi.org\/10.32604\/cmc.2019.05353","DOI":"10.32604\/cmc.2019.05353"},{"key":"19843_CR52","doi-asserted-by":"publisher","first-page":"21585","DOI":"10.1007\/s11042-019-7206-3","volume":"78","author":"ZF Elsharkawy","year":"2019","unstructured":"Elsharkawy ZF, Abdelwahab SAS, Abd El-Samie FE et al (2019) New and efficient blind detection algorithm for digital image forgery using homomorphic image processing. Multimed Tools Appl 78:21585\u201321611. https:\/\/doi.org\/10.1007\/s11042-019-7206-3","journal-title":"Multimed Tools Appl"},{"key":"19843_CR53","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10586-017-1668-8","volume":"22","author":"AR Abrahim","year":"2019","unstructured":"Abrahim AR, Rahim MSM, Sulong GB (2019) Splicing image forgery identification based on artificial neural network approach and texture features. Cluster Comput 22:647\u2013660. https:\/\/doi.org\/10.1007\/s10586-017-1668-8","journal-title":"Cluster Comput"},{"key":"19843_CR54","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","volume":"28","author":"JH Bappy","year":"2019","unstructured":"Bappy JH, Simons C, Nataraj L et al (2019) Hybrid LSTM and encoder-decoder architecture for detection of image forgeries. IEEE Trans Image Process 28:3286\u20133300. https:\/\/doi.org\/10.1109\/TIP.2019.2895466","journal-title":"IEEE Trans Image Process"},{"key":"19843_CR55","doi-asserted-by":"publisher","first-page":"7867","DOI":"10.1007\/s00521-018-3586-y","volume":"31","author":"Z Moghaddasi","year":"2019","unstructured":"Moghaddasi Z, Jalab HA, Noor RMd (2019) Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients. Neural Comput Applic 31:7867\u20137877. https:\/\/doi.org\/10.1007\/s00521-018-3586-y","journal-title":"Neural Comput Applic"},{"key":"19843_CR56","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.eswa.2019.04.036","volume":"131","author":"E Odaba\u015f Y\u0131ld\u0131r\u0131m","year":"2019","unstructured":"Odaba\u015f Y\u0131ld\u0131r\u0131m E, Uluta\u015f G (2019) Augmented features to detect image splicing on SWT domain. Expert Syst Appl 131:81\u201393. https:\/\/doi.org\/10.1016\/j.eswa.2019.04.036","journal-title":"Expert Syst Appl"},{"key":"19843_CR57","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.3390\/sym11101223","volume":"11","author":"Wu Wei","year":"2019","unstructured":"Wei Wu, Dong, et al (2019) Developing an image manipulation detection algorithm based on edge detection and faster R-CNN. Symmetry 11:1223. https:\/\/doi.org\/10.3390\/sym11101223","journal-title":"Symmetry"},{"key":"19843_CR58","doi-asserted-by":"publisher","first-page":"1392","DOI":"10.3390\/sym11111392","volume":"11","author":"T Subramaniam","year":"2019","unstructured":"Subramaniam T, Jalab HA, Ibrahim RW, Mohd Noor NF (2019) Improved image splicing forgery detection by combination of conformable focus measures and focus measure operators applied on obtained redundant discrete wavelet transform coefficients. Symmetry 11:1392. https:\/\/doi.org\/10.3390\/sym11111392","journal-title":"Symmetry"},{"key":"19843_CR59","doi-asserted-by":"publisher","first-page":"129494","DOI":"10.1109\/ACCESS.2019.2939812","volume":"7","author":"K Zhang","year":"2019","unstructured":"Zhang K, Liang Y, Zhang J et al (2019) No one can escape: a general approach to detect tampered and generated image. IEEE Access 7:129494\u2013129503. https:\/\/doi.org\/10.1109\/ACCESS.2019.2939812","journal-title":"IEEE Access"},{"key":"19843_CR60","doi-asserted-by":"publisher","first-page":"92586","DOI":"10.1109\/ACCESS.2019.2927540","volume":"7","author":"KH Rhee","year":"2019","unstructured":"Rhee KH (2019) Forensic detection using bit-planes slicing of median filtering image. IEEE Access 7:92586\u201392597. https:\/\/doi.org\/10.1109\/ACCESS.2019.2927540","journal-title":"IEEE Access"},{"key":"19843_CR61","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.image.2019.05.003","volume":"76","author":"Q Zhang","year":"2019","unstructured":"Zhang Q, Xiao H, Xue F et al (2019) Digital image forensics of non-uniform deblurring. Signal Process: Image Commun 76:167\u2013177. https:\/\/doi.org\/10.1016\/j.image.2019.05.003","journal-title":"Signal Process: Image Commun"},{"key":"19843_CR62","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.jisa.2019.06.004","volume":"47","author":"J Wang","year":"2019","unstructured":"Wang J, Li Y, Li J et al (2019) Color image-spliced localization based on quaternion principal component analysis and quaternion skewness. J Inform Secur Appl 47:353\u2013362. https:\/\/doi.org\/10.1016\/j.jisa.2019.06.004","journal-title":"J Inform Secur Appl"},{"key":"19843_CR63","doi-asserted-by":"publisher","first-page":"6907","DOI":"10.3934\/mbe.2019346","volume":"16","author":"B Chen","year":"2019","unstructured":"Chen B, Gao Y, Xu L et al (2019) Color image splicing localization algorithm by quaternion fully convolutional networks and superpixel-enhanced pairwise conditional random field. MBE 16:6907\u20136922. https:\/\/doi.org\/10.3934\/mbe.2019346","journal-title":"MBE"},{"key":"19843_CR64","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1049\/iet-ipr.2019.1291","volume":"14","author":"A Thakur","year":"2020","unstructured":"Thakur A, Jindal N (2020) Hybrid deep learning and machine learning approach for passive image forensic. IET Image Proc 14:1952\u20131959. https:\/\/doi.org\/10.1049\/iet-ipr.2019.1291","journal-title":"IET Image Proc"},{"key":"19843_CR65","doi-asserted-by":"publisher","first-page":"188970","DOI":"10.1109\/ACCESS.2020.3029087","volume":"8","author":"KH Rhee","year":"2020","unstructured":"Rhee KH (2020) Composition of visual feature vector pattern for deep learning in image forensics. IEEE Access 8:188970\u2013188980. https:\/\/doi.org\/10.1109\/ACCESS.2020.3029087","journal-title":"IEEE Access"},{"key":"19843_CR66","doi-asserted-by":"publisher","first-page":"133488","DOI":"10.1109\/ACCESS.2020.3009877","volume":"8","author":"F Marra","year":"2020","unstructured":"Marra F, Gragnaniello D, Verdoliva L, Poggi G (2020) A Full-image full-resolution end-to-end-trainable CNN framework for image forgery detection. IEEE Access 8:133488\u2013133502. https:\/\/doi.org\/10.1109\/ACCESS.2020.3009877","journal-title":"IEEE Access"},{"key":"19843_CR67","doi-asserted-by":"publisher","first-page":"3379","DOI":"10.1007\/s13369-020-04401-0","volume":"45","author":"EI Abd El-Latif","year":"2020","unstructured":"Abd El-Latif EI, Taha A, Zayed HH (2020) A passive approach for detecting image splicing based on deep learning and wavelet transform. Arab J Sci Eng 45:3379\u20133386. https:\/\/doi.org\/10.1007\/s13369-020-04401-0","journal-title":"Arab J Sci Eng"},{"key":"19843_CR68","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1109\/TIFS.2019.2924552","volume":"15","author":"O Mayer","year":"2020","unstructured":"Mayer O, Stamm MC (2020) Forensic similarity for digital images. IEEE Trans Inf Forensics Secur 15:1331\u20131346. https:\/\/doi.org\/10.1109\/TIFS.2019.2924552","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR69","doi-asserted-by":"publisher","first-page":"3105","DOI":"10.1049\/iet-ipr.2019.1114","volume":"14","author":"A Mazumdar","year":"2020","unstructured":"Mazumdar A, Bora PK (2020) Siamese convolutional neural network-based approach towards universal image forensics. IET Image Proc 14:3105\u20133116. https:\/\/doi.org\/10.1049\/iet-ipr.2019.1114","journal-title":"IET Image Proc"},{"key":"19843_CR70","doi-asserted-by":"publisher","first-page":"1500","DOI":"10.3390\/electronics9091500","volume":"9","author":"MM Islam","year":"2020","unstructured":"Islam MM, Karmakar G, Kamruzzaman J, Murshed M (2020) A robust forgery detection method for copy-move and splicing attacks in images. Electronics 9:1500. https:\/\/doi.org\/10.3390\/electronics9091500","journal-title":"Electronics"},{"key":"19843_CR71","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 et al (2020) Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Inf Sci 511:172\u2013191. https:\/\/doi.org\/10.1016\/j.ins.2019.09.038","journal-title":"Inf Sci"},{"key":"19843_CR72","doi-asserted-by":"publisher","first-page":"106728","DOI":"10.1016\/j.asoc.2020.106728","volume":"97","author":"H Kasban","year":"2020","unstructured":"Kasban H, Nassar S (2020) An efficient approach for forgery detection in digital images using Hilbert\u2013Huang transform. Appl Soft Comput 97:106728. https:\/\/doi.org\/10.1016\/j.asoc.2020.106728","journal-title":"Appl Soft Comput"},{"key":"19843_CR73","doi-asserted-by":"publisher","first-page":"29977","DOI":"10.1007\/s11042-020-09415-2","volume":"79","author":"SP Jaiprakash","year":"2020","unstructured":"Jaiprakash SP, Desai MB, Prakash CS et al (2020) Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery. Multimed Tools Appl 79:29977\u201330005. https:\/\/doi.org\/10.1007\/s11042-020-09415-2","journal-title":"Multimed Tools Appl"},{"key":"19843_CR74","doi-asserted-by":"publisher","first-page":"8249","DOI":"10.1007\/s11042-019-08597-8","volume":"79","author":"N Alipour","year":"2020","unstructured":"Alipour N, Behrad A (2020) Semantic segmentation of JPEG blocks using a deep CNN for non-aligned JPEG forgery detection and localization. Multimed Tools Appl 79:8249\u20138265. https:\/\/doi.org\/10.1007\/s11042-019-08597-8","journal-title":"Multimed Tools Appl"},{"key":"19843_CR75","doi-asserted-by":"publisher","first-page":"32037","DOI":"10.1007\/s11042-020-09275-w","volume":"79","author":"N Kaur","year":"2020","unstructured":"Kaur N, Jindal N, Singh K (2020) A passive approach for the detection of splicing forgery in digital images. Multimed Tools Appl 79:32037\u201332063. https:\/\/doi.org\/10.1007\/s11042-020-09275-w","journal-title":"Multimed Tools Appl"},{"key":"19843_CR76","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1109\/TIFS.2019.2935913","volume":"15","author":"F Matern","year":"2020","unstructured":"Matern F, Riess C, Stamminger M (2020) Gradient-based illumination description for image forgery detection. IEEE Trans Inf Forensics Secur 15:1303\u20131317. https:\/\/doi.org\/10.1109\/TIFS.2019.2935913","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR77","doi-asserted-by":"publisher","first-page":"12829","DOI":"10.1007\/s11042-020-08621-2","volume":"79","author":"N Kanwal","year":"2020","unstructured":"Kanwal N, Girdhar A, Kaur L, Bhullar JS (2020) Digital image splicing detection technique using optimal threshold based local ternary pattern. Multimed Tools Appl 79:12829\u201312846. https:\/\/doi.org\/10.1007\/s11042-020-08621-2","journal-title":"Multimed Tools Appl"},{"key":"19843_CR78","doi-asserted-by":"publisher","first-page":"505","DOI":"10.3390\/app10020505","volume":"10","author":"LM Dang","year":"2020","unstructured":"Dang LM, Min K, Lee S et al (2020) Tampered and computer-generated face images identification based on deep learning. Appl Sci 10:505. https:\/\/doi.org\/10.3390\/app10020505","journal-title":"Appl Sci"},{"key":"19843_CR79","doi-asserted-by":"publisher","first-page":"11837","DOI":"10.1007\/s11042-019-08480-6","volume":"79","author":"AK Jaiswal","year":"2020","unstructured":"Jaiswal AK, Srivastava R (2020) A technique for image splicing detection using hybrid feature set. Multimed Tools Appl 79:11837\u201311860. https:\/\/doi.org\/10.1007\/s11042-019-08480-6","journal-title":"Multimed Tools Appl"},{"key":"19843_CR80","doi-asserted-by":"crossref","unstructured":"Kashyap A, Suresh B, Gupta H (2020) Detection of splicing forgery using differential evolution and wavelet decomposition. Computer J 63(11):1727\u20131737","DOI":"10.1093\/comjnl\/bxz107"},{"key":"19843_CR81","doi-asserted-by":"publisher","first-page":"103374","DOI":"10.1109\/ACCESS.2020.2999308","volume":"8","author":"KH Rhee","year":"2020","unstructured":"Rhee KH (2020) Detection of spliced image forensics using texture analysis of median filter residual. IEEE Access 8:103374\u2013103384. https:\/\/doi.org\/10.1109\/ACCESS.2020.2999308","journal-title":"IEEE Access"},{"key":"19843_CR82","doi-asserted-by":"publisher","first-page":"25611","DOI":"10.1109\/ACCESS.2020.2970735","volume":"8","author":"Y Rao","year":"2020","unstructured":"Rao Y, Ni J, Zhao H (2020) Deep learning local descriptor for image splicing detection and localization. IEEE Access 8:25611\u201325625. https:\/\/doi.org\/10.1109\/ACCESS.2020.2970735","journal-title":"IEEE Access"},{"key":"19843_CR83","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s11554-019-00893-8","volume":"17","author":"B Yang","year":"2020","unstructured":"Yang B, Li Z, Zhang T (2020) A real-time image forensics scheme based on multi-domain learning. J Real-Time Image Proc 17:29\u201340. https:\/\/doi.org\/10.1007\/s11554-019-00893-8","journal-title":"J Real-Time Image Proc"},{"key":"19843_CR84","doi-asserted-by":"publisher","first-page":"6729","DOI":"10.1109\/ACCESS.2019.2963745","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu Y, Zhao X (2020) Constrained image splicing detection and localization with attention-aware encoder-decoder and atrous convolution. IEEE Access 8:6729\u20136741. https:\/\/doi.org\/10.1109\/ACCESS.2019.2963745","journal-title":"IEEE Access"},{"key":"19843_CR85","doi-asserted-by":"publisher","first-page":"26139","DOI":"10.1007\/s11042-020-09280-z","volume":"79","author":"H Zeng","year":"2020","unstructured":"Zeng H, Peng A, Lin X (2020) Exposing image splicing with inconsistent sensor noise levels. Multimed Tools Appl 79:26139\u201326154. https:\/\/doi.org\/10.1007\/s11042-020-09280-z","journal-title":"Multimed Tools Appl"},{"key":"19843_CR86","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.3906\/elk-2005-37","volume":"29","author":"A Doegar","year":"2021","unstructured":"Doegar A, Hiriyannaiah S, Matt S et al (2021) Image forgery detection based on fusion of lightweight deep learning models. Turk J Electr Eng Comput Sci 29:1978\u20131993. https:\/\/doi.org\/10.3906\/elk-2005-37","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"19843_CR87","doi-asserted-by":"publisher","first-page":"99742","DOI":"10.1109\/ACCESS.2021.3096240","volume":"9","author":"S Walia","year":"2021","unstructured":"Walia S, Kumar K, Kumar M, Gao X-Z (2021) Fusion of handcrafted and deep features for forgery detection in digital images. IEEE Access 9:99742\u201399755. https:\/\/doi.org\/10.1109\/ACCESS.2021.3096240","journal-title":"IEEE Access"},{"key":"19843_CR88","doi-asserted-by":"publisher","unstructured":"Bibi S, Abbasi A, Haq IU, Baik SW, Ullah A (2021) Digital image forgery detection using deep autoencoder and CNN features. Hum Cent Comput Inf Sci 11(2021):32.\u00a0https:\/\/doi.org\/10.22967\/HCIS.2021.11.032","DOI":"10.22967\/HCIS.2021.11.032"},{"key":"19843_CR89","doi-asserted-by":"publisher","unstructured":"Wei Y, Wang Z, Xiao B, et al (2020) Controlling neural learning network with multiple scales for image splicing forgery detection. ACM Trans Multimedia Comput Commun Appl 16:124:1\u2013124:22. https:\/\/doi.org\/10.1145\/3408299","DOI":"10.1145\/3408299"},{"key":"19843_CR90","doi-asserted-by":"publisher","first-page":"5397","DOI":"10.1109\/TIFS.2021.3129654","volume":"16","author":"Y Niu","year":"2021","unstructured":"Niu Y, Tondi B, Zhao Y et al (2021) Image splicing detection, localization and attribution via JPEG primary quantization matrix estimation and clustering. IEEE Trans Inf Forensics Secur 16:5397\u20135412. https:\/\/doi.org\/10.1109\/TIFS.2021.3129654","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR91","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.3390\/electronics10182255","volume":"10","author":"C Yu","year":"2021","unstructured":"Yu C, Zhou J, Li Q (2021) Multi-supervised encoder-decoder for image forgery localization. Electronics 10:2255. https:\/\/doi.org\/10.3390\/electronics10182255","journal-title":"Electronics"},{"key":"19843_CR92","doi-asserted-by":"publisher","first-page":"115630","DOI":"10.1016\/j.eswa.2021.115630","volume":"185","author":"R Mehta","year":"2021","unstructured":"Mehta R, Aggarwal K, Koundal D et al (2021) Markov features based DTCWS algorithm for online image forgery detection using ensemble classifier in the pandemic. Expert Syst Appl 185:115630. https:\/\/doi.org\/10.1016\/j.eswa.2021.115630","journal-title":"Expert Syst Appl"},{"key":"19843_CR93","doi-asserted-by":"publisher","first-page":"3571","DOI":"10.1007\/s11042-020-09816-3","volume":"80","author":"JN Abhishek","year":"2021","unstructured":"Abhishek JN (2021) Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation. Multimed Tools Appl 80:3571\u20133599. https:\/\/doi.org\/10.1007\/s11042-020-09816-3","journal-title":"Multimed Tools Appl"},{"key":"19843_CR94","doi-asserted-by":"publisher","first-page":"6503","DOI":"10.1002\/int.22558","volume":"36","author":"V Vinolin","year":"2021","unstructured":"Vinolin V, Sucharitha M (2021) Taylor-RNet: An approach for image forgery detection using Taylor-adaptive rag-bull rider-based deep convolutional neural network. Int J Intell Syst 36:6503\u20136530. https:\/\/doi.org\/10.1002\/int.22558","journal-title":"Int J Intell Syst"},{"key":"19843_CR95","doi-asserted-by":"publisher","unstructured":"Gill S, Sheikh N, Rajper S, et al (2021) Extended forgery detection framework for COVID-19 medical data using convolutional neural network. Comput Mater Continua 68:3773\u20133787. https:\/\/doi.org\/10.32604\/cmc.2021.016001","DOI":"10.32604\/cmc.2021.016001"},{"key":"19843_CR96","doi-asserted-by":"publisher","unstructured":"Al-Azawi R, Al-Saidi N, Jalab H, et al (2021) Image splicing detection based on texture features with fractal entropy. Comput Mater Continua 69:3903\u20133915. https:\/\/doi.org\/10.32604\/cmc.2021.020368","DOI":"10.32604\/cmc.2021.020368"},{"key":"19843_CR97","doi-asserted-by":"publisher","first-page":"5347","DOI":"10.3233\/JIFS-189857","volume":"41","author":"K Remya Revi","year":"2021","unstructured":"Remya Revi K, Wilscy M, Antony R et al (2021) Portrait photography splicing detection using ensemble of convolutional neural networks. J Intell Fuzzy Syst 41:5347\u20135357. https:\/\/doi.org\/10.3233\/JIFS-189857","journal-title":"J Intell Fuzzy Syst"},{"key":"19843_CR98","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1007\/s11042-020-09707-7","volume":"80","author":"P Niyishaka","year":"2021","unstructured":"Niyishaka P, Bhagvati C (2021) Image splicing detection technique based on Illumination-reflectance model and LBP. Multimed Tools Appl 80:2161\u20132175. https:\/\/doi.org\/10.1007\/s11042-020-09707-7","journal-title":"Multimed Tools Appl"},{"key":"19843_CR99","doi-asserted-by":"publisher","first-page":"561","DOI":"10.3906\/elk-2001-138","volume":"29","author":"N Kaur","year":"2021","unstructured":"Kaur N, Jindal N, Singh K (2021) Efficient hybrid passive method for the detection and localization of copy-move and spliced images. Turk J Electr Eng Comput Sci 29:561\u2013582. https:\/\/doi.org\/10.3906\/elk-2001-138","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"19843_CR100","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1049\/cje.2021.08.004","volume":"30","author":"C Beijing","year":"2021","unstructured":"Beijing C, Xingwang J, Ye G, Jinwei W (2021) A quaternion two-stream R-CNN Network for pixel-level color image splicing localization. Chin J Electron 30:1069\u20131079. https:\/\/doi.org\/10.1049\/cje.2021.08.004","journal-title":"Chin J Electron"},{"key":"19843_CR101","doi-asserted-by":"publisher","first-page":"8437","DOI":"10.3390\/app11188437","volume":"11","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Shen X, Liu S et al (2021) Image splicing location based on illumination maps and cluster region proposal network. Appl Sci 11:8437. https:\/\/doi.org\/10.3390\/app11188437","journal-title":"Appl Sci"},{"key":"19843_CR102","doi-asserted-by":"publisher","first-page":"91921","DOI":"10.1109\/ACCESS.2021.3091161","volume":"9","author":"CW Park","year":"2021","unstructured":"Park CW, Moon YH, Eom IK (2021) Image tampering localization using demosaicing patterns and singular value based prediction residue. IEEE Access 9:91921\u201391933. https:\/\/doi.org\/10.1109\/ACCESS.2021.3091161","journal-title":"IEEE Access"},{"key":"19843_CR103","doi-asserted-by":"publisher","DOI":"10.1108\/dta-10-2020-0234","author":"V Vinolin","year":"2022","unstructured":"Vinolin V, Sucharitha M (2022) Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment. Data Technol Appl. https:\/\/doi.org\/10.1108\/dta-10-2020-0234","journal-title":"Data Technol Appl"},{"key":"19843_CR104","doi-asserted-by":"publisher","unstructured":"Baumy A, Algarni A, Abdalla M, et al (2021) Efficient forgery detection approaches for digital color images. Comput Mater Continua 71:3257\u20133276. https:\/\/doi.org\/10.32604\/cmc.2022.021047","DOI":"10.32604\/cmc.2022.021047"},{"key":"19843_CR105","doi-asserted-by":"publisher","first-page":"101805","DOI":"10.1016\/j.jksus.2021.101805","volume":"34","author":"HA Jalab","year":"2022","unstructured":"Jalab HA, Alqarni MA, Ibrahim RW, Ali Almazroi A (2022) A novel pixel\u2019s fractional mean-based image enhancement algorithm for better image splicing detection. J King Saud Univ - Sci 34:101805. https:\/\/doi.org\/10.1016\/j.jksus.2021.101805","journal-title":"J King Saud Univ - Sci"},{"key":"19843_CR106","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3390\/e24010118","volume":"24","author":"Y Sun","year":"2022","unstructured":"Sun Y, Ni R, Zhao Y (2022) MFAN: multi-level features attention network for fake certificate image detection. Entropy 24:118. https:\/\/doi.org\/10.3390\/e24010118","journal-title":"Entropy"},{"key":"19843_CR107","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s11760-016-0899-0","volume":"11","author":"A Alahmadi","year":"2017","unstructured":"Alahmadi A, Hussain M, Aboalsamh H et al (2017) Passive detection of image forgery using DCT and local binary pattern. SIViP 11:81\u201388. https:\/\/doi.org\/10.1007\/s11760-016-0899-0","journal-title":"SIViP"},{"key":"19843_CR108","doi-asserted-by":"publisher","first-page":"18139","DOI":"10.1007\/s11042-017-5206-8","volume":"77","author":"H Yao","year":"2018","unstructured":"Yao H, Cao F, Tang Z et al (2018) Expose noise level inconsistency incorporating the inhomogeneity scoring strategy. Multimed Tools Appl 77:18139\u201318161. https:\/\/doi.org\/10.1007\/s11042-017-5206-8","journal-title":"Multimed Tools Appl"},{"key":"19843_CR109","doi-asserted-by":"publisher","unstructured":"Ahmed B, Gulliver TA, alZahir S (2020) Image splicing detection using mask-RCNN. SIViP 14:1035\u20131042.https:\/\/doi.org\/10.1007\/s11760-020-01636-0","DOI":"10.1007\/s11760-020-01636-0"},{"key":"19843_CR110","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06329-4","author":"H Ding","year":"2021","unstructured":"Ding H, Chen L, Tao Q et al (2021) DCU-Net: a dual-channel U-shaped network for image splicing forgery detection. Neural Comput Applic. https:\/\/doi.org\/10.1007\/s00521-021-06329-4","journal-title":"Neural Comput Applic"},{"key":"19843_CR111","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1007\/s11760-021-01895-5","volume":"15","author":"S Nath","year":"2021","unstructured":"Nath S, Naskar R (2021) Automated image splicing detection using deep CNN-learned features and ANN-based classifier. SIViP 15:1601\u20131608. https:\/\/doi.org\/10.1007\/s11760-021-01895-5","journal-title":"SIViP"},{"key":"19843_CR112","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.forsciint.2018.11.025","volume":"295","author":"VT Manu","year":"2019","unstructured":"Manu VT, Mehtre BM (2019) Tamper detection of social media images using quality artifacts and texture features. Forensic Sci Int 295:100\u2013112. https:\/\/doi.org\/10.1016\/j.forsciint.2018.11.025","journal-title":"Forensic Sci Int"},{"key":"19843_CR113","doi-asserted-by":"publisher","first-page":"115778","DOI":"10.1016\/j.image.2020.115778","volume":"82","author":"S Dua","year":"2020","unstructured":"Dua S, Singh J, Parthasarathy H (2020) Detection and localization of forgery using statistics of DCT and Fourier components. Signal Process: Image Commun 82:115778. https:\/\/doi.org\/10.1016\/j.image.2020.115778","journal-title":"Signal Process: Image Commun"},{"key":"19843_CR114","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ins.2020.03.099","volume":"526","author":"B Liu","year":"2020","unstructured":"Liu B, Pun C-M (2020) Exposing splicing forgery in realistic scenes using deep fusion network. Inf Sci 526:133\u2013150. https:\/\/doi.org\/10.1016\/j.ins.2020.03.099","journal-title":"Inf Sci"},{"key":"19843_CR115","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.procs.2020.04.038","volume":"171","author":"S Dua","year":"2020","unstructured":"Dua S, Singh J, Parthasarathy H (2020) Image forgery detection based on statistical features of block DCT coefficients. Procedia Comput Sci 171:369\u2013378. https:\/\/doi.org\/10.1016\/j.procs.2020.04.038","journal-title":"Procedia Comput Sci"},{"key":"19843_CR116","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.neucom.2019.12.105","volume":"387","author":"B Liu","year":"2020","unstructured":"Liu B, Pun C-M (2020) Locating splicing forgery by adaptive-SVD noise estimation and vicinity noise descriptor. Neurocomput 387:172\u2013187. https:\/\/doi.org\/10.1016\/j.neucom.2019.12.105","journal-title":"Neurocomput"},{"key":"19843_CR117","doi-asserted-by":"publisher","first-page":"102967","DOI":"10.1016\/j.jvcir.2020.102967","volume":"73","author":"B Chen","year":"2020","unstructured":"Chen B, Qi X, Zhou Y et al (2020) Image splicing localization using residual image and residual-based fully convolutional network. J Vis Commun Image Represent 73:102967. https:\/\/doi.org\/10.1016\/j.jvcir.2020.102967","journal-title":"J Vis Commun Image Represent"},{"key":"19843_CR118","doi-asserted-by":"publisher","first-page":"108051","DOI":"10.1016\/j.sigpro.2021.108051","volume":"183","author":"Y Rao","year":"2021","unstructured":"Rao Y, Ni J, Xie H (2021) Multi-semantic CRF-based attention model for image forgery detection and localization. Signal Process 183:108051. https:\/\/doi.org\/10.1016\/j.sigpro.2021.108051","journal-title":"Signal Process"},{"key":"19843_CR119","doi-asserted-by":"publisher","first-page":"107733","DOI":"10.1016\/j.cie.2021.107733","volume":"162","author":"B Singh","year":"2021","unstructured":"Singh B, Sharma DK (2021) SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural network. Comput Ind Eng 162:107733. https:\/\/doi.org\/10.1016\/j.cie.2021.107733","journal-title":"Comput Ind Eng"},{"key":"19843_CR120","doi-asserted-by":"publisher","first-page":"76437","DOI":"10.1109\/ACCESS.2018.2883588","volume":"6","author":"Z Shi","year":"2018","unstructured":"Shi Z, Shen X, Kang H, Lv Y (2018) Image manipulation detection and localization based on the dual-domain convolutional neural networks. IEEE Access 6:76437\u201376453. https:\/\/doi.org\/10.1109\/ACCESS.2018.2883588","journal-title":"IEEE Access"},{"key":"19843_CR121","doi-asserted-by":"publisher","unstructured":"Walia S, Kumar K (2019) Characterization of splicing in digital images using gray scale co-occurrence matrices. In: 2019 twelfth international conference on contemporary computing (IC3).\u00a0IEEE Explore, pp 1\u20136. https:\/\/doi.org\/10.1109\/IC3.2019.8844881","DOI":"10.1109\/IC3.2019.8844881"},{"key":"19843_CR122","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1109\/TIFS.2019.2902826","volume":"14","author":"Y Liu","year":"2019","unstructured":"Liu Y, Zhu X, Zhao X, Cao Y (2019) Adversarial learning for constrained image splicing detection and localization based on atrous convolution. IEEE Trans Inf Forensics Secur 14:2551\u20132566. https:\/\/doi.org\/10.1109\/TIFS.2019.2902826","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"19843_CR123","doi-asserted-by":"publisher","unstructured":"Wei B, Yu M, Chen K, Jiang J (2019) Deep-BIF: blind image forensics based on deep learning. In: 2019 IEEE conference on dependable and secure computing (DSC), Hangzhou, China, pp 1\u20136. https:\/\/doi.org\/10.1109\/DSC47296.2019.8937712","DOI":"10.1109\/DSC47296.2019.8937712"},{"key":"19843_CR124","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1109\/JSTSP.2020.3001516","volume":"14","author":"O Mayer","year":"2020","unstructured":"Mayer O, Stamm MC (2020) Exposing fake images with forensic similarity graphs. IEEE J Select Topics in Signal Process 14:1049\u20131064. https:\/\/doi.org\/10.1109\/JSTSP.2020.3001516","journal-title":"IEEE J Select Topics in Signal Process"},{"key":"19843_CR125","doi-asserted-by":"publisher","unstructured":"Hebbar NK, Kunte AS (2021) Image forgery localization using u-net based architecture and error level analysis. In: 2021 3rd international conference on advances in computing, communication control and networking (ICAC3N).\u00a0IEEE Xplore, Greater Noida, India, pp 1992\u20131996. https:\/\/doi.org\/10.1109\/ICAC3N53548.2021.9725373","DOI":"10.1109\/ICAC3N53548.2021.9725373"},{"key":"19843_CR126","doi-asserted-by":"publisher","first-page":"162499","DOI":"10.1109\/ACCESS.2021.3130342","volume":"9","author":"K Kadam","year":"2021","unstructured":"Kadam K, Ahirrao S, Kotecha K, Sahu S (2021) Detection and localization of multiple image splicing using mobilenet V1. IEEE Access 9:162499\u2013162519. https:\/\/doi.org\/10.1109\/ACCESS.2021.3130342","journal-title":"IEEE Access"},{"key":"19843_CR127","doi-asserted-by":"crossref","unstructured":"Schuld M, Petruccione F (2021) Machine learning with quantum computers. In: Barnes & Noble. https:\/\/www.barnesandnoble.com\/w\/machine-learning-with-quantum-computers-maria-schuld\/1139757793. Accessed 5\/3\/2023","DOI":"10.1007\/978-3-030-83098-4"},{"key":"19843_CR128","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/11766247_37","volume-title":"Advances in artificial intelligence","author":"E A\u00efmeur","year":"2006","unstructured":"A\u00efmeur E, Brassard G, Gambs S (2006) Machine learning in a quantum world. In: Lamontagne L, Marchand M (eds) Advances in artificial intelligence. Springer, Berlin, pp 431\u2013442"},{"key":"19843_CR129","unstructured":"Bajaj A (2021) Performance metrics in machine learning [complete guide]. In: neptune.ai. https:\/\/neptune.ai\/blog\/performance-metrics-in-machine-learning-complete-guide.\u00a0Accessed 5\/3\/2023"},{"key":"19843_CR130","unstructured":"Zach (2021) How to calculate Matthews correlation coefficient in python. In: Statology. https:\/\/www.statology.org\/matthews-correlation-coefficient-python\/"},{"key":"19843_CR131","unstructured":"Rosebrock A (2016) Intersection over Union (IoU) for object detection. In: PyImageSearch. https:\/\/pyimagesearch.com\/2016\/11\/07\/intersection-over-union-iou-for-object-detection\/"},{"key":"19843_CR132","unstructured":"Bhandari A (2020) Guide to AUC ROC curve in machine learning\u202f: what is specificity? In: Analytics Vidhya. https:\/\/www.analyticsvidhya.com\/blog\/2020\/06\/auc-roc-curve-machine-learning\/"},{"key":"19843_CR133","unstructured":"NIST MediFor Team (2017) NIST nimble challenge 2017 evaluation plan 2017\u201308\u201304:https:\/\/www.nist.gov\/system\/files\/documents\/2017\/09\/07\/nc2017evaluationplan_20170804.pdf"},{"key":"19843_CR134","unstructured":"Zach (2020) How to calculate Root Mean Square Error (RMSE) in excel. In: statology. https:\/\/www.statology.org\/root-mean-square-error-excel\/"},{"key":"19843_CR135","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s42484-021-00061-x","volume":"4","author":"T Hur","year":"2022","unstructured":"Hur T, Kim L, Park DK (2022) Quantum convolutional neural network for classical data classification. Quantum Machine Intell 4:3. https:\/\/doi.org\/10.1007\/s42484-021-00061-x","journal-title":"Quantum Machine Intell"},{"key":"19843_CR136","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-7098-1","volume-title":"Quantum machine learning an applied approach: the theory and application of quantum machine learning in science and industry","author":"S Ganguly","year":"2021","unstructured":"Ganguly S (2021) Quantum machine learning an applied approach: the theory and application of quantum machine learning in science and industry. Apress, Berkeley"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19843-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19843-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19843-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T13:09:13Z","timestamp":1751375353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19843-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":136,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["19843"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19843-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,7,23]]},"assertion":[{"value":"11 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","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 authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}