{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T23:58:45Z","timestamp":1770681525678,"version":"3.49.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The Key Research and Development Project of Heilongjiang Province","award":["2022ZX01A34"],"award-info":[{"award-number":["2022ZX01A34"]}]},{"name":"The 2020 Heilongjiang Province Higher Education Teaching Reform Project","award":["SJGY 20200320"],"award-info":[{"award-number":["SJGY 20200320"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18751-6","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:01:50Z","timestamp":1711594910000},"page":"647-663","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FLAG: frequency-based local and global network for face forgery detection"],"prefix":"10.1007","volume":"84","author":[{"given":"Kai","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2589-1164","authenticated-orcid":false,"given":"Guanglu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiahui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Linsen","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"18751_CR1","unstructured":"Pu Y, Gan Z, Henao R, Yuan X, Li C, Stevens A, Carin L (2016) Variational autoencoder for deep learning of images, labels and captions. Advan Neural Inform Process Syst 29"},{"key":"18751_CR2","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advan Neural Inform Process Syst 27"},{"key":"18751_CR3","unstructured":"Citron DK (2019) How deepfakes undermine truth and threaten democracy. https:\/\/www.ted.com"},{"key":"18751_CR4","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","volume":"64","author":"R Tolosana","year":"2020","unstructured":"Tolosana R, Vera-Rodriguez R, Fierrez J, Morales A, Ortega-Garcia J (2020) Deepfakes and beyond: a survey of face manipulation and fake detection. Inform Fusion 64:131\u2013148","journal-title":"Inform Fusion"},{"key":"18751_CR5","first-page":"2638","volume":"35","author":"K Sun","year":"2021","unstructured":"Sun K, Liu H, Ye Q, Gao Y, Liu J, Shao L, Ji R (2021) Domain general face forgery detection by learning to weight. Proc AAAI Conf Artif Intell 35:2638\u20132646","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"18751_CR6","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TIFS.2022.3198275","volume":"17","author":"C Miao","year":"2022","unstructured":"Miao C, Tan Z, Chu Q, Yu N, Guo G (2022) Hierarchical frequency-assisted interactive networks for face manipulation detection. IEEE Trans Inf Forensics Secur 17:3008\u20133021","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"18751_CR7","doi-asserted-by":"crossref","unstructured":"Wang J, Wu Z, Ouyang W, Han X, Chen J, Jiang Y-G, Li S-N (2022) M2TR: multi-modal multi-scale transformers for deepfake detection. In: Proceedings of the 2022 international conference on multimedia retrieval, pp 615\u2013623","DOI":"10.1145\/3512527.3531415"},{"key":"18751_CR8","doi-asserted-by":"crossref","unstructured":"Wang J, Tondi B, Barni M (2022) An eyes-based Siamese neural network for the detection of GAN-generated face images. Front Signal Process 2:918725","DOI":"10.3389\/frsip.2022.918725"},{"key":"18751_CR9","doi-asserted-by":"crossref","unstructured":"Wang J, Alamayreh O, Tondi B, Costanzo A, Barni M et\u00a0al (2022) Detecting deepfake videos in data scarcity conditions by means of video coding features. APSIPA Trans Signal Inform Process 11(2)","DOI":"10.1561\/116.00000032"},{"key":"18751_CR10","doi-asserted-by":"crossref","unstructured":"Afchar D, Nozick V, Yamagishi J, Echizen I (2018) Mesonet: a compact facial video forgery detection network. In: 2018 IEEE international workshop on information forensics and security (WIFS), pp 1\u20137. IEEE","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"18751_CR11","doi-asserted-by":"crossref","unstructured":"Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nie\u00dfner M (2019) Faceforensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1\u201311","DOI":"10.1109\/ICCV.2019.00009"},{"key":"18751_CR12","doi-asserted-by":"crossref","unstructured":"Matern F, Riess C, Stamminger M (2019) Exploiting visual artifacts to expose deepfakes and face manipulations. In: 2019 IEEE winter applications of computer vision workshops (WACVW), pp 83\u201392. IEEE","DOI":"10.1109\/WACVW.2019.00020"},{"key":"18751_CR13","doi-asserted-by":"crossref","unstructured":"Ni Y, Meng D, Yu C, Quan C, Ren D, Zhao Y (2022) CORE: consistent representation learning for face forgery detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12\u201321","DOI":"10.1109\/CVPRW56347.2022.00011"},{"key":"18751_CR14","doi-asserted-by":"crossref","unstructured":"Wang P, Liu K, Zhou W, Zhou H, Liu H, Zhang W, Yu N (2022) ADT: anti-deepfake transformer. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2899\u20131903","DOI":"10.1109\/ICASSP43922.2022.9746888"},{"key":"18751_CR15","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et\u00a0al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929"},{"issue":"14","key":"18751_CR16","doi-asserted-by":"publisher","first-page":"21353","DOI":"10.1007\/s11042-022-13801-3","volume":"82","author":"E Arkin","year":"2023","unstructured":"Arkin E, Yadikar N, Xu X, Aysa A, Ubul K (2023) A survey: object detection methods from CNN to transformer. Multimed Tool Appl 82(14):21353\u201321383","journal-title":"Multimed Tool Appl"},{"key":"18751_CR17","unstructured":"Wodajo D, Atnafu S (2021) Deepfake video detection using convolutional vision transformer. arXiv:2102.11126"},{"key":"18751_CR18","doi-asserted-by":"crossref","unstructured":"Coccomini DA, Messina N, Gennaro C, Falchi F (2022) Combining efficientnet and vision transformers for video deepfake detection. In: International conference on image analysis and processing, pp 219\u2013229. Springer","DOI":"10.1007\/978-3-031-06433-3_19"},{"key":"18751_CR19","doi-asserted-by":"crossref","unstructured":"Yang X, Li Y, Lyu S (2019) Exposing deep fakes using inconsistent head poses. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 8261\u20138265. IEEE","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"18751_CR20","doi-asserted-by":"publisher","first-page":"4234","DOI":"10.1109\/TIFS.2021.3102487","volume":"16","author":"J Yang","year":"2021","unstructured":"Yang J, Li A, Xiao S, Lu W, Gao X (2021) MTD-Net: learning to detect deepfakes images by multi-scale texture difference. IEEE Trans Inf Forensics Secur 16:4234\u20134245","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"18751_CR21","unstructured":"Deepfakes (2022) GitHub. https:\/\/github.com\/deepfakes\/faceswap"},{"key":"18751_CR22","doi-asserted-by":"publisher","first-page":"18461","DOI":"10.1007\/s11042-020-10420-8","volume":"80","author":"A Kohli","year":"2021","unstructured":"Kohli A, Gupta A (2021) Detecting deepfake, faceswap and face2face facial forgeries using frequency CNN. Multimed Tool Appl 80:18461\u201318478","journal-title":"Multimed Tool Appl"},{"issue":"4","key":"18751_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3499026","volume":"18","author":"Y Yu","year":"2022","unstructured":"Yu Y, Ni R, Li W, Zhao Y (2022) Detection of AI-manipulated fake faces via mining generalized features. ACM Trans Multimed Comput Commun Appl 18(4):1\u201323","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"18751_CR24","doi-asserted-by":"crossref","unstructured":"Qian Y, Yin G, Sheng L, Chen Z, Shao J (2020) Thinking in frequency: face forgery detection by mining frequency-aware clues. In: European conference on computer vision, pp 86\u2013103. Springer","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"18751_CR25","doi-asserted-by":"crossref","unstructured":"Luo Y, Zhang Y, Yan J, Liu W (2021) Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16317\u201316326","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"18751_CR26","first-page":"1081","volume":"35","author":"S Chen","year":"2021","unstructured":"Chen S, Yao T, Chen Y, Ding S, Li J, Ji R (2021) Local relation learning for face forgery detection. Proc AAAI Conf Artif Intell 35:1081\u20131088","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"18751_CR27","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"18751_CR28","doi-asserted-by":"crossref","unstructured":"Qin Z, Zhang P, Wu F, Li X (2021) FcaNet: frequency channel attention networks. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 783\u2013792","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"18751_CR29","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.patrec.2018.08.009","volume":"130","author":"W Wan","year":"2020","unstructured":"Wan W, Wang J, Li J, Meng L, Sun J, Zhang H, Liu J (2020) Pattern complexity-based JND estimation for quantization watermarking. Pattern Recogn Lett 130:157\u2013164","journal-title":"Pattern Recogn Lett"},{"key":"18751_CR30","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"issue":"4","key":"18751_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies J, Zollh\u00f6fer M, Nie\u00dfner M (2019) Deferred neural rendering: image synthesis using neural textures. Acm Trans Graphics (TOG) 38(4):1\u201312","journal-title":"Acm Trans Graphics (TOG)"},{"issue":"3","key":"18751_CR32","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TIFS.2012.2190402","volume":"7","author":"J Fridrich","year":"2012","unstructured":"Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868\u2013882","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"4","key":"18751_CR33","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1109\/TIFS.2015.2506548","volume":"11","author":"T Carvalho","year":"2015","unstructured":"Carvalho T, Faria FA, Pedrini H, Torres RdS, Rocha A (2015) Illuminant-based transformed spaces for image forensics. IEEE Trans Inform Forensics Secur 11(4):720\u2013733","journal-title":"IEEE Trans Inform Forensics Secur"},{"issue":"2","key":"18751_CR34","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/TIFS.2016.2623589","volume":"12","author":"B Peng","year":"2016","unstructured":"Peng B, Wang W, Dong J, Tan T (2016) Optimized 3D lighting environment estimation for image forgery detection. IEEE Trans Inf Forensics Secur 12(2):479\u2013494","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"18751_CR35","doi-asserted-by":"crossref","unstructured":"Cozzolino D, Poggi G, Verdoliva L (2017) Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: Proceedings of the 5th ACM workshop on information hiding and multimedia security, pp 159\u2013164","DOI":"10.1145\/3082031.3083247"},{"key":"18751_CR36","doi-asserted-by":"crossref","unstructured":"Li L, Bao J, Zhang T, Yang H, Chen D, Wen F, Guo B (2020) Face x-ray for more general face forgery detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5001\u20135010","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"18751_CR37","doi-asserted-by":"crossref","unstructured":"Zhao H, Zhou W, Chen D, Wei T, Zhang W, Yu N (2021) Multi-attentional deepfake detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2185\u20132194","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"18751_CR38","doi-asserted-by":"crossref","unstructured":"Dong S, Wang J, Liang J, Fan H, Ji R (2022) Explaining deepfake detection by analysing image matching. In: European conference on computer vision, pp 18\u201335. Springer","DOI":"10.1007\/978-3-031-19781-9_2"},{"key":"18751_CR39","unstructured":"Frank J, Eisenhofer T, Sch\u00f6nherr L, Fischer A, Kolossa D, Holz T (2020) Leveraging frequency analysis for deep fake image recognition. In: International conference on machine learning, pp 3247\u20133258. PMLR"},{"key":"18751_CR40","doi-asserted-by":"crossref","unstructured":"Liu H, Li X, Zhou W, Chen Y, He Y, Xue H, Zhang W, Yu N (2021) Spatial-phase shallow learning: rethinking face forgery detection in frequency domain. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 772\u2013781","DOI":"10.1109\/CVPR46437.2021.00083"},{"issue":"1","key":"18751_CR41","doi-asserted-by":"publisher","first-page":"5575","DOI":"10.1038\/s41467-020-19266-y","volume":"11","author":"IV Tetko","year":"2020","unstructured":"Tetko IV, Karpov P, Van Deursen R, Godin G (2020) State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat Commun 11(1):5575","journal-title":"Nat Commun"},{"issue":"3","key":"18751_CR42","doi-asserted-by":"publisher","first-page":"3713","DOI":"10.1007\/s11042-022-13428-4","volume":"82","author":"D Khurana","year":"2023","unstructured":"Khurana D, Koli A, Khatter K, Singh S (2023) Natural language processing: state of the art, current trends and challenges. Multimed Tool Appl 82(3):3713\u20133744","journal-title":"Multimed Tool Appl"},{"key":"18751_CR43","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision, pp 213\u2013229. Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"18751_CR44","doi-asserted-by":"crossref","unstructured":"Li Y, Mao H, Girshick R, He K (2022) Exploring plain vision transformer backbones for object detection. In: European conference on computer vision, pp 280\u2013296. Springer","DOI":"10.1007\/978-3-031-20077-9_17"},{"key":"18751_CR45","first-page":"1","volume":"60","author":"K Xu","year":"2022","unstructured":"Xu K, Deng P, Huang H (2022) Vision transformer: an excellent teacher for guiding small networks in remote sensing image scene classification. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"18751_CR46","doi-asserted-by":"crossref","unstructured":"Dan J, Liu Y, Xie H, Deng J, Xie H, Xie X, Sun B (2023) TransFace: calibrating transformer training for face recognition from a data-centric perspective. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 20642\u201320653","DOI":"10.1109\/ICCV51070.2023.01887"},{"key":"18751_CR47","first-page":"30392","volume":"34","author":"T Xiao","year":"2021","unstructured":"Xiao T, Singh M, Mintun E, Darrell T, Doll\u00e1r P, Girshick R (2021) Early convolutions help transformers see better. Adv Neural Inf Process Syst 34:30392\u201330400","journal-title":"Adv Neural Inf Process Syst"},{"key":"18751_CR48","doi-asserted-by":"crossref","unstructured":"Li Y, Yang X, Sun P, Qi H, Lyu S (2020) Celeb-DF: a large-scale challenging dataset for deepfake forensics. In: CVPR, pp 3207\u20133216","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"18751_CR49","unstructured":"Dolhansky B, Bitton J, Pflaum B, Lu J, Howes R, Wang M, Ferrer CC (2020) The deepfake detection challenge (DFDC) dataset. arXiv:2006.07397"},{"key":"18751_CR50","doi-asserted-by":"crossref","unstructured":"Thies J, Zollhofer M, Stamminger M, Theobalt C, Nie\u00dfner M (2016) Face2face: real-time face capture and reenactment of RGB videos. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2387\u20132395","DOI":"10.1109\/CVPR.2016.262"},{"key":"18751_CR51","unstructured":"Faceswap (2019) GitHub. http:\/\/www.github.com\/MarekKowalski"},{"issue":"10","key":"18751_CR52","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"18751_CR53","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp 6105\u20136114. PMLR"},{"key":"18751_CR54","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition, pp 248\u2013255. IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"18751_CR55","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1109\/TIFS.2022.3146781","volume":"17","author":"P Yu","year":"2022","unstructured":"Yu P, Fei J, Xia Z, Zhou Z, Weng J (2022) Improving generalization by commonality learning in face forgery detection. IEEE Trans Inf Forensics Secur 17:547\u2013558","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"18751_CR56","unstructured":"Cozzolino D, Thies J, R\u00f6ssler A, Riess C, Nie\u00dfner M, Verdoliva L (2018) Forensictransfer: weakly-supervised domain adaptation for forgery detection. arXiv:1812.02510"},{"key":"18751_CR57","doi-asserted-by":"crossref","unstructured":"Nguyen HH, Fang F, Yamagishi J, Echizen I (2019) Multi-task learning for detecting and segmenting manipulated facial images and videos. In: 2019 IEEE 10th international conference on biometrics theory, applications and systems (BTAS), pp 1\u20138. IEEE","DOI":"10.1109\/BTAS46853.2019.9185974"},{"key":"18751_CR58","doi-asserted-by":"crossref","unstructured":"Li D, Yang Y, Song Y-Z, Hospedales T (2018) Learning to generalize: meta-learning for domain generalization. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"18751_CR59","doi-asserted-by":"crossref","unstructured":"Dong X, Bao J, Chen D, Zhang T, Zhang W, Yu N, Chen D, Wen F, Guo B (2022) Protecting celebrities from deepfake with identity consistency transformer. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9468\u20139478","DOI":"10.1109\/CVPR52688.2022.00925"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18751-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18751-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18751-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T13:55:18Z","timestamp":1737986118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18751-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,28]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["18751"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18751-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,28]]},"assertion":[{"value":"23 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The Author confirms: that the work described has not been published before; that it is not under consideration for publication elsewhere; that its publication has been approved by all co-authors; that its publication has been approved by the responsible authorities at the institution where the work is carried out.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}