{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:32:33Z","timestamp":1770748353730,"version":"3.50.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"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\/s11042-023-17028-8","type":"journal-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T05:02:22Z","timestamp":1697000542000},"page":"41311-41325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A sequential convolutional neural network for image forgery detection"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0434-6995","authenticated-orcid":false,"given":"Simranjot","family":"Kaur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumit","family":"Chopra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anchal","family":"Nayyar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajesh","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gagandeep","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"issue":"5","key":"17028_CR1","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/JSTSP.2020.3002101","volume":"14","author":"L Verdoliva","year":"2020","unstructured":"Verdoliva L (2020) Media forensics and deepfakes: an overview. IEEE J Sel Top Signal Process 14(5):910\u2013932","journal-title":"IEEE J Sel Top Signal Process"},{"key":"17028_CR2","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde- Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. arXiv preprint arXiv:1406.2661, 1(1):910\u2013932"},{"key":"17028_CR3","unstructured":"Fridrich AJ, Soukal BD, Luk\u00e1\u0161 AJ (2003) Detection of copymove forgery in digital images, in in Proceedings of Digital Forensic Research Workshop, vol 1, no 1. Citeseer, p 403"},{"issue":"1","key":"17028_CR4","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s11042-010-0620-1","volume":"51","author":"JA Redi","year":"2011","unstructured":"Redi JA, Taktak W, Dugelay J-L (2011) Digital image forensics: a booklet for beginners. Multimedia Tools Appl 51(1):133\u2013162","journal-title":"Multimedia Tools Appl"},{"key":"17028_CR5","doi-asserted-by":"crossref","unstructured":"Bharti CN, Tandel P (2016) A survey of image forgery detection techniques. In: 2016 International conference on wireless communications, signal processing and networking (WiSPNET), vol 1, no 1. IEEE, pp 877\u2013881","DOI":"10.1109\/WiSPNET.2016.7566257"},{"key":"17028_CR6","doi-asserted-by":"crossref","unstructured":"Kaur S, Rani R (2022) Image forgery detection using multi-layer convolutional neural network. in Advanced Machine Intelligence and Signal Processing. Springer, pp 855\u2013866","DOI":"10.1007\/978-981-19-0840-8_66"},{"issue":"4","key":"17028_CR7","doi-asserted-by":"publisher","first-page":"686","DOI":"10.3390\/diagnostics13040686","volume":"13","author":"S Fekri-Ershad","year":"2023","unstructured":"Fekri-Ershad S, Alsaffar MF (2023) Developing a tuned three-layer perceptron fed with trained deep convolutional neural networks for cervical cancer diagnosis. Diagnostics 13(4):686","journal-title":"Diagnostics"},{"key":"17028_CR8","doi-asserted-by":"crossref","unstructured":"Al Azrak FM, Sedik A, Dessowky MI, El Banby GM, Khalaf AA, Elkorany AS, El\u2013Samie FEA (2020) An efficient method for image forgery detection based on trigonometric transforms and deep learning. Multimedia Tools Appl 79(25):18 221\u201318 243","DOI":"10.1007\/s11042-019-08162-3"},{"issue":"2","key":"17028_CR9","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MSP.2008.931079","volume":"26","author":"H Farid","year":"2009","unstructured":"Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16\u201325","journal-title":"IEEE Signal Process Mag"},{"issue":"6","key":"17028_CR10","doi-asserted-by":"publisher","first-page":"3495","DOI":"10.1007\/s11042-015-2449-0","volume":"75","author":"W-C Hu","year":"2016","unstructured":"Hu W-C, Chen W-H, Huang D-Y, Yang C-Y (2016) Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes. Multimedia Tools Appl 75(6):3495\u20133516","journal-title":"Multimedia Tools Appl"},{"issue":"3","key":"17028_CR11","doi-asserted-by":"publisher","first-page":"403","DOI":"10.3390\/electronics11030403","volume":"11","author":"SS Ali","year":"2022","unstructured":"Ali SS, Ganapathi II, Vu N-S, Ali SD, Saxena N, Werghi N (2022) Image forgery detection using deep learning by recompressing images. Electronics 11(3):403","journal-title":"Electronics"},{"issue":"5","key":"17028_CR12","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1504\/IJESDF.2022.125403","volume":"14","author":"S Kaur","year":"2022","unstructured":"Kaur S, Rani R, Garg R, Sharma N (2022) State-of-the-art techniques for passive image forgery detection: a brief review. Int J Electron Secur Digit Forensics 14(5):456\u2013473","journal-title":"Int J Electron Secur Digit Forensics"},{"issue":"3","key":"17028_CR13","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.diin.2013.04.007","volume":"10","author":"GK Birajdar","year":"2013","unstructured":"Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: A survey. Digit Investig 10(3):226\u2013245","journal-title":"Digit Investig"},{"issue":"1","key":"17028_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2020.110311","volume":"1","author":"R Thakur","year":"2020","unstructured":"Thakur R, Rohilla R (2020) Recent advances in digital image manipulation detection techniques: A brief review. Forensic Sci Int 1(1):110311","journal-title":"Forensic Sci Int"},{"key":"17028_CR15","doi-asserted-by":"crossref","unstructured":"Elaskily MA, Aslan HK, Elshakankiry OA, Faragallah OS, Abd El-Samie FE, Dessouky MM (2017) Comparative study of copymove forgery detection techniques. In: 2017 Intl Conf on Advanced Control Circuits Systems (ACCS) Systems & 2017 Intl Conf on New Paradigms in Electronics & Information Technology (PEIT), vol 1, no 1. IEEE, pp 193\u2013203","DOI":"10.1109\/ACCS-PEIT.2017.8303041"},{"issue":"11","key":"17028_CR16","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.3390\/sym12111811","volume":"12","author":"S Bourouis","year":"2020","unstructured":"Bourouis S, Alroobaea R, Alharbi AM, Andejany M, Rubaiee S (2020) Recent advances in digital multimedia tampering detection for forensics analysis. Symmetry 12(11):1811","journal-title":"Symmetry"},{"issue":"1","key":"17028_CR17","first-page":"1","volume":"1","author":"MA Elaskily","year":"2020","unstructured":"Elaskily MA, Elnemr HA, Sedik A, Dessouky MM, El Banby GM, Elshakankiry OA, Khalaf AA, Aslan HK, Faragallah OS, Abd El-Samie FE (2020) A novel deep learning framework for copy-moveforgery detection in images. Multimedia Tools Appl 1(1):1\u201326","journal-title":"Multimedia Tools Appl"},{"key":"17028_CR18","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.jnca.2016.09.008","volume":"75","author":"NB Abd Warif","year":"2016","unstructured":"Abd Warif NB, Wahab AWA, Idris MYI, Ramli R, Salleh R, Shamshirband S, Choo K-KR (2016) Copy-move forgery detection: survey, challenges and future directions. J Netw Comput Appl 75:259\u2013278","journal-title":"J Netw Comput Appl"},{"issue":"10","key":"17028_CR19","doi-asserted-by":"publisher","first-page":"6714","DOI":"10.1109\/TII.2020.2982705","volume":"16","author":"Y Zhu","year":"2020","unstructured":"Zhu Y, Chen C, Yan G, Guo Y, Dong Y (2020) Ar-net: Adaptive attention and residual refinement network for copy-move forgery detection. IEEE Trans Ind Inform 16(10):6714\u20136723","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"17028_CR20","first-page":"1053","volume":"1","author":"P Zhou","year":"2018","unstructured":"Zhou P, Han X, Morariu VI, Davis LS (2018) Learning rich features for image manipulation detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1(1):1053\u20131061","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"issue":"7","key":"17028_CR21","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, Manjunath B, Roy-Chowdhury AK (2019) Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286\u20133300","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"17028_CR22","first-page":"1","volume":"1","author":"R Agarwal","year":"2019","unstructured":"Agarwal R, Verma OP (2019) An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimedia Tools Appl 1(1):1\u201322","journal-title":"Multimedia Tools Appl"},{"issue":"9","key":"17028_CR23","doi-asserted-by":"publisher","first-page":"286","DOI":"10.3390\/info10090286","volume":"10","author":"Y Abdalla","year":"2019","unstructured":"Abdalla Y, Iqbal MT, Shehata M (2019) Copy-move forgery detection and localization using a generative adversarial network and convolutional neural-network. Information 10(9):286","journal-title":"Information"},{"issue":"1","key":"17028_CR24","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1007\/s11042-022-12391-4","volume":"82","author":"S Kumar","year":"2023","unstructured":"Kumar S, Mukherjee S, Pal AK (2023) An improved reduced featurebased copy-move forgery detection technique. Multimedia Tools Appl 82(1):1431\u20131456","journal-title":"Multimedia Tools Appl"},{"key":"17028_CR25","doi-asserted-by":"crossref","unstructured":"Wang X-Y, Wang X-Q, Niu P-P, Yang H-Y (2023) Accurate and robust image copy-move forgery detection using adaptive keypoints and fqgpcet-glcm feature. Multimedia Tools Appl 1\u201333","DOI":"10.1007\/s11042-023-15499-3"},{"issue":"7","key":"17028_CR26","doi-asserted-by":"publisher","first-page":"10061","DOI":"10.1007\/s11042-022-12311-6","volume":"82","author":"ST Babu","year":"2023","unstructured":"Babu ST, Rao CS (2023) Efficient detection of copy-move forgery using polar complex exponential transform and gradient direction pattern. Multimedia Tools Appl 82(7):10061\u201310075","journal-title":"Multimedia Tools Appl"},{"key":"17028_CR27","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 1(1):403"},{"key":"17028_CR28","doi-asserted-by":"crossref","unstructured":"Wen B, Zhu Y, Subramanian R, Ng T-T, Shen X, Winkler S (2016) Coverage\u2013a novel database for copy-move forgery detection. In: 2016 IEEE international conference on image processing (ICIP), 1(1). IEEE, pp 161\u2013165","DOI":"10.1109\/ICIP.2016.7532339"},{"issue":"5","key":"17028_CR29","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1109\/TIFS.2012.2202227","volume":"7","author":"P Ferrara","year":"2012","unstructured":"Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Transactions on Information Forensics and Security 7(5):1566\u20131577","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"10","key":"17028_CR30","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1016\/j.imavis.2009.02.001","volume":"27","author":"B Mahdian","year":"2009","unstructured":"Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image Vision Comput 27(10):1497\u20131503","journal-title":"Image Vision Comput"},{"issue":"1","key":"17028_CR31","first-page":"4970","volume":"1","author":"JH Bappy","year":"2017","unstructured":"Bappy JH, Roy-Chowdhury AK, Bunk J, Nataraj L, Manjunath B (2017) Exploiting spatial structure for localizing manipulated image regions. Proceedings of the IEEE international conference on computer vision 1(1):4970\u20134979","journal-title":"Proceedings of the IEEE international conference on computer vision"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17028-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17028-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17028-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T13:23:19Z","timestamp":1712236999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17028-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,11]]},"references-count":31,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["17028"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17028-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,11]]},"assertion":[{"value":"21 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}