{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:24:40Z","timestamp":1775024680841,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s12530-021-09367-4","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T11:03:19Z","timestamp":1614855799000},"page":"27-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0420-1892","authenticated-orcid":false,"given":"Ritu","family":"Agarwal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Om Prakash","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"9367_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2663-3","author":"MH Alkawaz","year":"2018","unstructured":"Alkawaz MH, Sulong G, Saba T, Rehman A (2018) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-016-2663-3","journal-title":"Neural Comput Appl"},{"key":"9367_CR2","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1109\/TIFS.2011.2129512","volume":"6","author":"I Amerini","year":"2011","unstructured":"Amerini I, Ballan L, Caldelli R et al (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6:1099\u20131110. https:\/\/doi.org\/10.1109\/TIFS.2011.2129512","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"9367_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2013.03.006","author":"I Amerini","year":"2013","unstructured":"Amerini I, Ballan L, Caldelli R et al (2013) Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process Image Commun. https:\/\/doi.org\/10.1016\/j.image.2013.03.006","journal-title":"Signal Process Image Commun"},{"key":"9367_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113856","author":"S Askari","year":"2020","unstructured":"Askari S (2020) Fuzzy C-means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: review and development. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.113856","journal-title":"Expert Syst Appl"},{"key":"9367_CR5","doi-asserted-by":"crossref","unstructured":"Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings.","DOI":"10.1109\/ICASSP.2009.4959768"},{"key":"9367_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-4276-3","author":"XL Bi","year":"2018","unstructured":"Bi XL, Pun CM, Yuan XC (2018) Multi-scale feature extraction and adaptive matching for copy-move forgery detection. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-016-4276-3","journal-title":"Multimed Tools Appl"},{"key":"9367_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2019.10.029","author":"Y Cao","year":"2019","unstructured":"Cao Y, Wu Y, Fu L et al (2019) Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Rep. https:\/\/doi.org\/10.1016\/j.egyr.2019.10.029","journal-title":"Energy Rep"},{"key":"9367_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/designs2030028","author":"G Dhiman","year":"2018","unstructured":"Dhiman G, Kaur A (2018) Optimizing the design of airfoil and optical buffer problems using spotted hyena optimizer. Designs. https:\/\/doi.org\/10.3390\/designs2030028","journal-title":"Designs"},{"key":"9367_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.03.021","author":"G Dhiman","year":"2019","unstructured":"Dhiman G, Kaur A (2019) STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Eng Appl Artif Intell. https:\/\/doi.org\/10.1016\/j.engappai.2019.03.021","journal-title":"Eng Appl Artif Intell"},{"key":"9367_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.05.014","author":"G Dhiman","year":"2017","unstructured":"Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw. https:\/\/doi.org\/10.1016\/j.advengsoft.2017.05.014","journal-title":"Adv Eng Softw"},{"key":"9367_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.06.001","author":"G Dhiman","year":"2018","unstructured":"Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2018.06.001","journal-title":"Knowl-Based Syst"},{"key":"9367_CR12","doi-asserted-by":"publisher","DOI":"10.1002\/int.22105","author":"F Di Martino","year":"2019","unstructured":"Di Martino F, Senatore S, Sessa S (2019) A lightweight clustering\u2013based approach to discover different emotional shades from social message streams. Int J Intell Syst. https:\/\/doi.org\/10.1002\/int.22105","journal-title":"Int J Intell Syst"},{"key":"9367_CR13","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2016.0537","author":"R Dixit","year":"2017","unstructured":"Dixit R, Naskar R, Mishra S (2017) Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD. IET Image Process. https:\/\/doi.org\/10.1049\/iet-ipr.2016.0537","journal-title":"IET Image Process"},{"key":"9367_CR01","doi-asserted-by":"crossref","unstructured":"Fan DP, Gong C, Cao Y, Ren B, Cheng MM, Borji A (2018) Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp 698\u2013704","DOI":"10.24963\/ijcai.2018\/97"},{"key":"9367_CR14","doi-asserted-by":"publisher","DOI":"10.1145\/358669.358692","author":"MA Fischler","year":"1981","unstructured":"Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM. https:\/\/doi.org\/10.1145\/358669.358692","journal-title":"Commun ACM"},{"key":"9367_CR15","first-page":"4","volume":"3","author":"J Fridrich","year":"2003","unstructured":"Fridrich J, Soukal D, Luk\u00e1\u0161 J (2003) Detection of copy-move forgery in digital images. Int J 3:4","journal-title":"Int J"},{"key":"9367_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2006.12.015","author":"J Han","year":"2007","unstructured":"Han J, Ma KK (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis Comput. https:\/\/doi.org\/10.1016\/j.imavis.2006.12.015","journal-title":"Image Vis Comput"},{"key":"9367_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2016.11.034","author":"Z Hossein-Nejad","year":"2017","unstructured":"Hossein-Nejad Z, Nasri M (2017) An adaptive image registration method based on SIFT features and RANSAC transform. Comput Electr Eng. https:\/\/doi.org\/10.1016\/j.compeleceng.2016.11.034","journal-title":"Comput Electr Eng"},{"key":"9367_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2942064","author":"H Jia","year":"2019","unstructured":"Jia H, Sun K, Song W et al (2019) Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using masi entropy. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2942064","journal-title":"IEEE Access"},{"key":"9367_CR19","doi-asserted-by":"crossref","unstructured":"Levinshtein A, Stere A, Kutulakos KN, et al (2009) TurboPixels: Fast superpixels using geometric flows. In: IEEE Transactions on Pattern Analysis and Machine Intelligence.","DOI":"10.1109\/TPAMI.2009.96"},{"key":"9367_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s12555-019-0438-1","author":"M Li","year":"2020","unstructured":"Li M, Hu S, Xia J et al (2020) Dissolved oxygen model predictive control for activated sludge process model based on the Fuzzy C-means cluster algorithm. Int J Control Autom Syst. https:\/\/doi.org\/10.1007\/s12555-019-0438-1","journal-title":"Int J Control Autom Syst"},{"key":"9367_CR21","doi-asserted-by":"crossref","unstructured":"Liu Z, Meur L, Luo S (2013) Superpixel-based saliency detection. In: International Workshop on Image Analysis for Multimedia Interactive Services.","DOI":"10.1109\/WIAMIS.2013.6616119"},{"key":"9367_CR22","unstructured":"Popescu AC, Farid H (2004) Exposing Digital Forgeries by Detecting Duplicated Image Regions. Tech Report, TR2004\u2013515, Dep Comput Sci Dartmouth Coll Hanover, New Hampsh,pp 1\u201311"},{"key":"9367_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2423261","author":"CM Pun","year":"2015","unstructured":"Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur. https:\/\/doi.org\/10.1109\/TIFS.2015.2423261","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"9367_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2015.08.008","author":"MA Qureshi","year":"2015","unstructured":"Qureshi MA, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun. https:\/\/doi.org\/10.1016\/j.image.2015.08.008","journal-title":"Signal Process Image Commun"},{"key":"9367_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2018.11.004","author":"PM Raju","year":"2018","unstructured":"Raju PM, Nair MS (2018) Copy-move forgery detection using binary discriminant features. J King Saud Univ Comput Inf Sci. https:\/\/doi.org\/10.1016\/j.jksuci.2018.11.004","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"9367_CR26","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2013.2272377","author":"SJ Ryu","year":"2013","unstructured":"Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans Inf Forensics Secur. https:\/\/doi.org\/10.1109\/TIFS.2013.2272377","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"9367_CR27","first-page":"199","volume":"8","author":"BL Shivakumar","year":"2011","unstructured":"Shivakumar BL, Baboo S (2011) Detection of Region Duplication Forgery in Digital Images Using SURF. Int J Comput Sci Issues 8:199\u2013205","journal-title":"Int J Comput Sci Issues"},{"key":"9367_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2014.11.005","author":"X Tian","year":"2015","unstructured":"Tian X, Jiao L, Yi L et al (2015) The image segmentation based on optimized spatial feature of superpixel. J Vis Commun Image Represent. https:\/\/doi.org\/10.1016\/j.jvcir.2014.11.005","journal-title":"J Vis Commun Image Represent"},{"key":"9367_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-016-0588-1","author":"XY Wang","year":"2018","unstructured":"Wang XY, Liu YN, Xu H et al (2018) Robust copy\u2013move forgery detection using quaternion exponent moments. Pattern Anal Appl. https:\/\/doi.org\/10.1007\/s10044-016-0588-1","journal-title":"Pattern Anal Appl"},{"key":"9367_CR30","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.engappai.2016.12.022","volume":"59","author":"F Yang","year":"2017","unstructured":"Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73\u201383. https:\/\/doi.org\/10.1016\/j.engappai.2016.12.022","journal-title":"Eng Appl Artif Intell"},{"key":"9367_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-016-4289-y","author":"B Yang","year":"2018","unstructured":"Yang B, Sun X, Guo H et al (2018) A copy-move forgery detection method based on CMFD-SIFT. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-016-4289-y","journal-title":"Multimed Tools Appl"},{"key":"9367_CR32","doi-asserted-by":"crossref","unstructured":"Yohannan RP, Manuel M (2016) Detection of copy-move forgery based on Gabor filter. In: Proceedings of 2nd IEEE International Conference on Engineering and Technology, ICETECH 2016.","DOI":"10.1109\/ICETECH.2016.7569326"},{"key":"9367_CR33","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1007\/s11045-016-0416-1","volume":"27","author":"J Zheng","year":"2016","unstructured":"Zheng J, Liu Y, Ren J et al (2016) Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimens Syst Signal Process 27:989\u20131005. https:\/\/doi.org\/10.1007\/s11045-016-0416-1","journal-title":"Multidimens Syst Signal Process"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-021-09367-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-021-09367-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-021-09367-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T21:29:35Z","timestamp":1644269375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-021-09367-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["9367"],"URL":"https:\/\/doi.org\/10.1007\/s12530-021-09367-4","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]},"assertion":[{"value":"5 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}