{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:34:34Z","timestamp":1780479274153,"version":"3.54.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Natural Science Foundation of Anhui Province of China","award":["2008085MF220"],"award-info":[{"award-number":["2008085MF220"]}]},{"name":"School Foundation of Anhui University of Science and Technology","award":["2021CX2102"],"award-info":[{"award-number":["2021CX2102"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s10044-024-01391-9","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T05:44:43Z","timestamp":1734673483000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploiting optimized forgery representation space for general fake face detection"],"prefix":"10.1007","volume":"28","author":[{"given":"Gaoming","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bang","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianjin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"issue":"7","key":"1391_CR1","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1007\/s11263-022-01606-8","volume":"130","author":"F Juefei-Xu","year":"2022","unstructured":"Juefei-Xu F, Wang R, Huang Y, Guo Q, Ma L, Liu Y (2022) Countering malicious deepfakes: survey, battleground, and horizon. Int J Comput Vis 130(7):1678\u20131734. https:\/\/doi.org\/10.1007\/s11263-022-01606-8","journal-title":"Int J Comput Vis"},{"issue":"3","key":"1391_CR2","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s00371-021-02347-4","volume":"39","author":"S Tyagi","year":"2023","unstructured":"Tyagi S, Yadav D (2023) A detailed analysis of image and video forgery detection techniques. Vis Comput 39(3):813\u2013833. https:\/\/doi.org\/10.1007\/s00371-021-02347-4","journal-title":"Vis Comput"},{"key":"1391_CR3","doi-asserted-by":"publisher","unstructured":"R\u00f6ssler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Niessner M (2019) FaceForensics++: learning to detect manipulated facial images. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp 1\u201311 . https:\/\/doi.org\/10.1109\/ICCV.2019.00009","DOI":"10.1109\/ICCV.2019.00009"},{"key":"1391_CR4","doi-asserted-by":"publisher","unstructured":"Li, L, Bao J, Zhang T, Yang H, Chen D, Wen F, Guo B (2020) Face X-ray for more general face forgery fetection. In: IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5000\u20135009. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00505","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"1391_CR5","doi-asserted-by":"publisher","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 (ECCV), pp 86\u2013103. https:\/\/doi.org\/10.1007\/978-3-030-58610-2_6","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"1391_CR6","doi-asserted-by":"publisher","DOI":"10.1145\/3558004","author":"X Liu","year":"2022","unstructured":"Liu X, Yu Y, Li X, Zhao Y, Guo G (2022) TCSD: triple complementary streams detector for comprehensive deepfake detection. ACM Trans Multimed Comput Commun Appl. https:\/\/doi.org\/10.1145\/3558004","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"1391_CR7","doi-asserted-by":"publisher","unstructured":"Luo Y, Zhang Y, Yan J, Liu W (2021) Generalizing face forgery detection with high-frequency features. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 16317\u201316326. https:\/\/doi.org\/10.1109\/CVPR46437.2021.01605","DOI":"10.1109\/CVPR46437.2021.01605"},{"key":"1391_CR8","unstructured":"Zhao N, Wu Z, Lau RWH, Lin S (2021) What makes instance discrimination good for transfer learning? In: International Conference on Learning Representations (ICLR)"},{"key":"1391_CR9","doi-asserted-by":"crossref","unstructured":"Hua T, Wang W, Xue Z, Ren S, Wang Y, Zhao H (2021) On feature decorrelation in self-supervised learning. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp 9598\u20139608","DOI":"10.1109\/ICCV48922.2021.00946"},{"key":"1391_CR10","unstructured":"Bardes A, Ponce J, Lecun Y (2022) VICReg: variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations (ICLR)"},{"key":"1391_CR11","doi-asserted-by":"publisher","unstructured":"Zhou P, Han X, Morariu VI, Davis LS (2017) Two-stream neural networks for tampered face detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1831\u20131839 . https:\/\/doi.org\/10.1109\/CVPRW.2017.229","DOI":"10.1109\/CVPRW.2017.229"},{"key":"1391_CR12","doi-asserted-by":"publisher","unstructured":"Masi I, Killekar A, Mascarenhas RM, Gurudatt SP, AbdAlmageed W (2020) Two-branch recurrent network for isolating deepfakes in videos. In: European Conference on Computer Vision (ECCV), pp 667\u2013684. https:\/\/doi.org\/10.1007\/978-3-030-58571-6_39","DOI":"10.1007\/978-3-030-58571-6_39"},{"key":"1391_CR13","doi-asserted-by":"publisher","unstructured":"Afchar D, Nozick V, Yamagishi J, Echizen I (2018) MesoNet: a compact facial video forgery detection network. In: IEEE International Workshop on Information Forensics and Security (WIFS), pp 1\u20137. https:\/\/doi.org\/10.1109\/WIFS.2018.8630761","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"1391_CR14","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. https:\/\/doi.org\/10.1109\/TIFS.2022.3146781","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1391_CR15","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.neucom.2022.06.013","volume":"501","author":"M Yu","year":"2022","unstructured":"Yu M, Ju S, Zhang J, Li S, Lei J, Li X (2022) Patch-DFD: patch-based end-to-end deepfake discriminator. Neurocomputing 501:583\u2013595. https:\/\/doi.org\/10.1016\/j.neucom.2022.06.013","journal-title":"Neurocomputing"},{"key":"1391_CR16","doi-asserted-by":"publisher","unstructured":"Zhang B, Li S, Feng G, Qian Z, Zhang X (2022) Patch diffusion: a general module for face manipulation detection. In: AAAI Conference on Artificial Intelligence, vol. 36, pp 3243\u20133251. https:\/\/doi.org\/10.1609\/aaai.v36i3.20233","DOI":"10.1609\/aaai.v36i3.20233"},{"key":"1391_CR17","doi-asserted-by":"publisher","unstructured":"Wang J, Wu Z, Ouyang W, Han X, Chen J, Jiang Y, Li S-N (2022) M2TR: multi-modal multi-scale transformers for deepfake detection. In: International Conference on Multimedia Retrieval, pp 615\u2013623. https:\/\/doi.org\/10.1145\/3512527.3531415","DOI":"10.1145\/3512527.3531415"},{"key":"1391_CR18","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30"},{"key":"1391_CR19","doi-asserted-by":"publisher","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: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 772\u2013781. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00083","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"1391_CR20","doi-asserted-by":"publisher","unstructured":"Zhao H, Zhou W, Chen D, Wei T, Zhang W, Yu N (2021) Multi-attentional deepfake detection. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 2185\u20132194. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00222","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"1391_CR21","doi-asserted-by":"publisher","unstructured":"Gu Q, Chen S, Yao T, Chen Y, Ding S, Yi R (2022) Exploiting fine-grained face forgery clues via progressive enhancement learning. In: AAAI Conference on Artificial Intelligence, vol. 36, pp 735\u2013743. https:\/\/doi.org\/10.1609\/aaai.v36i1.19954","DOI":"10.1609\/aaai.v36i1.19954"},{"key":"1391_CR22","doi-asserted-by":"publisher","DOI":"10.1145\/3536426","author":"S Ge","year":"2022","unstructured":"Ge S, Lin F, Li C, Zhang D, Wang W, Zeng D (2022) Deepfake video detection via predictive representation learning. ACM Trans Multimed Comput Commun Appl. https:\/\/doi.org\/10.1145\/3536426","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"issue":"6356","key":"1391_CR23","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1038\/355161a0","volume":"355","author":"S Becker","year":"1992","unstructured":"Becker S, Hinton GE (1992) Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature 355(6356):161\u2013163. https:\/\/doi.org\/10.1038\/355161a0","journal-title":"Nature"},{"key":"1391_CR24","doi-asserted-by":"publisher","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick RB (2020) Momentum contrast for unsupervised visual representation learning. In: IEEE\/CVF Conference on Applications of Computer Vision (CVPR), pp 9726\u20139735. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00975","DOI":"10.1109\/CVPR42600.2020.00975"},{"issue":"2","key":"1391_CR25","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/TASE.2023.3265649","volume":"21","author":"D Li","year":"2024","unstructured":"Li D, Lu J, Zhang T, Ding J (2024) Self-supervised learning and multisource heterogeneous information fusion based quality anomaly detection for heavy-plate shape. IEEE Trans Autom Sci Eng 21(2):1223\u20131234. https:\/\/doi.org\/10.1109\/TASE.2023.3265649","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"1391_CR26","unstructured":"Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. In: Advances in Neural Information Processing Systems, vol. 33, pp 18661\u201318673"},{"key":"1391_CR27","doi-asserted-by":"crossref","unstructured":"Xu Y, Raja K, Pedersen M (2022) Supervised contrastive learning for generalizable and explainable deepfakes detection. In: IEEE\/CVF Conference on Applications of Computer Vision (CVPR), pp 379\u2013389","DOI":"10.1109\/WACVW54805.2022.00044"},{"key":"1391_CR28","doi-asserted-by":"publisher","unstructured":"Garrido Q, Chen Y, Bardes A, Najman L, Lecun Y (2022) On the duality between contrastive and non-contrastive self-supervised learning. CoRR. https:\/\/doi.org\/10.48550\/arXiv.2206.02574","DOI":"10.48550\/arXiv.2206.02574"},{"key":"1391_CR29","doi-asserted-by":"publisher","unstructured":"Lin T, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp 2999\u20133007 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"1391_CR30","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: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 3207\u20133216","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"1391_CR31","unstructured":"Li L, Bao J, Yang H, Chen D, Wen F (2019) FaceShifter: towards high fidelity and occlusion aware face swapping. CoRR"},{"key":"1391_CR32","unstructured":"Brian D, Russ H, Ben P, Nicole B, Cristian CF (2019) The Deepfake detection challenge (DFDC) preview dataset. CoRR"},{"issue":"2","key":"1391_CR33","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1109\/TIP.2017.2765830","volume":"27","author":"X Yin","year":"2018","unstructured":"Yin X, Liu X (2018) Multi-task convolutional neural network for pose-invariant face recognition. IEEE Trans Image Process 27(2):964\u2013975. https:\/\/doi.org\/10.1109\/TIP.2017.2765830","journal-title":"IEEE Trans Image Process"},{"key":"1391_CR34","unstructured":"Grill JB, Strub F, Altch\u2019e F, Tallec C, Richemond PH, Buchatskaya E, Doersch C, Pires BA, Guo ZD, Azar MG, Piot B, Kavukcuoglu K, Munos R, Valko M (2020) Bootstrap your own latent: a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, pp 21271\u201321284"},{"key":"1391_CR35","first-page":"6105","volume":"97","author":"M Tan","year":"2019","unstructured":"Tan M, Le Q (2019) EfficientNet: rethinking model scaling for convolutional neural networks. Int Conf Mach Learn 97:6105\u20136114","journal-title":"Int Conf Mach Learn"},{"key":"1391_CR36","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li LJ, Kai L, Li F-F (2009) ImageNet: a large-scale hierarchical image database. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1391_CR37","unstructured":"Loshchilov I, Hutter F (2017) SGDR: stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR)"},{"key":"1391_CR38","doi-asserted-by":"publisher","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1800\u20131807. https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"1391_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107950","volume":"116","author":"Z Shang","year":"2021","unstructured":"Shang Z, Xie H, Zha Z, Yu L, Li Y, Zhang Y (2021) PRRNet: pixel-region relation network for face forgery detection. Pattern Recogn 116:107950. https:\/\/doi.org\/10.1016\/j.patcog.2021.107950","journal-title":"Pattern Recogn"},{"key":"1391_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3186803","author":"J Wang","year":"2022","unstructured":"Wang J, Sun Y, Tang J (2022) LiSiam: localization invariance Siamese network for deepfake detection. IEEE Trans Inf Forensics Secur. https:\/\/doi.org\/10.1109\/TIFS.2022.3186803","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1391_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04462-2","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Jin X, Gao S, Wu L, Yao S, Jiang Q (2023) TAN-GFD: generalizing face forgery detection based on texture information and adaptive noise mining. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-023-04462-2","journal-title":"Appl Intell"},{"issue":"3","key":"1391_CR42","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. https:\/\/doi.org\/10.1109\/TIFS.2012.2190402","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1391_CR43","doi-asserted-by":"publisher","unstructured":"Cozzolino D, Poggi G, Verdoliva L (2017) Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: ACM Workshop on Information Hiding and Multimedia Security, pp 159\u2013164. https:\/\/doi.org\/10.1145\/3082031.3083247","DOI":"10.1145\/3082031.3083247"},{"key":"1391_CR44","doi-asserted-by":"publisher","unstructured":"Rahmouni N, Nozick V, Yamagishi J, Echizen I (2017) Distinguishing computer graphics from natural images using convolution neural networks. In: IEEE International Workshop on Information Forensics and Security (WIFS), pp 1\u20136. https:\/\/doi.org\/10.1109\/WIFS.2017.8267647","DOI":"10.1109\/WIFS.2017.8267647"},{"key":"1391_CR45","doi-asserted-by":"publisher","unstructured":"Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: ACM Workshop on Information Hiding and Multimedia Security, pp 5\u201310. https:\/\/doi.org\/10.1145\/2909827.2930786","DOI":"10.1145\/2909827.2930786"},{"key":"1391_CR46","unstructured":"Li Y, Lyu S (2019) Exposing deepfake videos by detecting face warping artifacts. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 46\u201352"},{"key":"1391_CR47","doi-asserted-by":"publisher","unstructured":"Li D, Yang Y, Song Y, Hospedales T (2018) Learning to generalize: meta-learning for domain generalization. In: AAAI Conference on Artificial Intelligence, vol. 32. https:\/\/doi.org\/10.1609\/aaai.v32i1.11596","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"1391_CR48","doi-asserted-by":"publisher","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. In: AAAI Conference on Artificial Intelligence, vol. 35, pp 2638\u20132646. https:\/\/doi.org\/10.1609\/aaai.v35i3.16367","DOI":"10.1609\/aaai.v35i3.16367"},{"key":"1391_CR49","doi-asserted-by":"publisher","unstructured":"Chen S, Yao T, Chen Y, Ding S, Li J, Ji R (2021) Local relation learning for face forgery detection. In: AAAI Conference on Artificial Intelligence, vol. 35, pp 1081\u20131088. https:\/\/doi.org\/10.1609\/aaai.v35i2.16193","DOI":"10.1609\/aaai.v35i2.16193"},{"issue":"11","key":"1391_CR50","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"issue":"2","key":"1391_CR51","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128(2):336\u2013359. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int J Comput Vis"},{"key":"1391_CR52","doi-asserted-by":"crossref","unstructured":"Collins E, Achanta R, Susstrunk S (2018) Deep feature factorization for concept discovery. In: European Conference on Computer Vision (ECCV), pp 336\u2013352","DOI":"10.1007\/978-3-030-01264-9_21"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-024-01391-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-024-01391-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-024-01391-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T20:10:22Z","timestamp":1739304622000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-024-01391-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["1391"],"URL":"https:\/\/doi.org\/10.1007\/s10044-024-01391-9","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]},"assertion":[{"value":"30 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2024","order":3,"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":"Conflict of interest"}}],"article-number":"9"}}