{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:33:40Z","timestamp":1787315620833,"version":"3.56.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-025-02558-4","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T16:57:56Z","timestamp":1738342676000},"page":"5033-5052","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A multi-level classification and prediction of unbalanced dataset real and fake faces using hybrid CMNV2 and deep learning methods"],"prefix":"10.1007","volume":"16","author":[{"given":"B. Anil","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neeraj Kumar","family":"Misra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"issue":"1","key":"2558_CR1","first-page":"65","volume":"4","author":"C Kant","year":"2013","unstructured":"Kant C, Sharma N (2013) Fake face recognition using fusion of thermal imaging and skin elasticity. IJCSC 4(1):65\u201372","journal-title":"IJCSC"},{"issue":"2","key":"2558_CR2","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TIP.2013.2292332","volume":"23","author":"J Galbally","year":"2013","unstructured":"Galbally J, Marcel S, Fierrez J (2013) Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710\u2013724","journal-title":"IEEE Trans Image Process"},{"key":"2558_CR3","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1007\/s00500-015-2019-4","volume":"21","author":"M Cho","year":"2017","unstructured":"Cho M, Jeong Y (2017) Face recognition performance comparison between fake faces and live faces. Soft Comput 21:3429\u20133437","journal-title":"Soft Comput"},{"issue":"3","key":"2558_CR4","first-page":"13","volume":"2","author":"D Bhattacharyya","year":"2009","unstructured":"Bhattacharyya D, Ranjan R, Alisherov F, Minkyu C (2009) Biometric authentication: a review. Int J u- and e- Serv, Sci Technol 2(3):13\u201328","journal-title":"Int J u- and e- Serv, Sci Technol"},{"key":"2558_CR5","doi-asserted-by":"crossref","unstructured":"Hsu C-C, Lee C-Y, Zhuang Y-X (2018) Learning to detect fake face images in the wild. In: IEEE International Symposium on Computer, Consumer and Control (IS3C), pp. 388\u2013391","DOI":"10.1109\/IS3C.2018.00104"},{"key":"2558_CR6","unstructured":"Mansourifar H, Shi W (2020) One-shot gan generated fake face detection. arXiv preprint arXiv:2003.12244"},{"key":"2558_CR7","doi-asserted-by":"crossref","unstructured":"Tariq S, Lee S, Kim H, Shin Y, Woo, SS (2018) Detecting both machine and human created fake face images in the wild. In: Proceedings of the 2nd International Workshop on Multimedia Privacy and Security, pp. 81\u201387","DOI":"10.1145\/3267357.3267367"},{"issue":"7","key":"2558_CR8","doi-asserted-by":"publisher","first-page":"6662","DOI":"10.1109\/TCYB.2021.3079311","volume":"52","author":"B Sheng","year":"2021","unstructured":"Sheng B, Li P, Ali R, Chen CP (2021) Improving video temporal consistency via broad learning system. IEEE Trans Cybern 52(7):6662\u20136675","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"2558_CR9","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TII.2021.3085669","volume":"18","author":"J Li","year":"2021","unstructured":"Li J, Chen J, Sheng B, Li P, Yang P, Feng DD, Qi J (2021) Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans Industr Inf 18(1):163\u2013173","journal-title":"IEEE Trans Industr Inf"},{"key":"2558_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3293885","author":"Z Chen","year":"2023","unstructured":"Chen Z, Qiu G, Li P, Zhu L, Yang X, Sheng B (2023) Mngnas: distilling adaptive combination of multiple searched networks for one-shot neural architecture search. IEEE Trans Pattern Analy Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2023.3293885","journal-title":"IEEE Trans Pattern Analy Mach Intell"},{"key":"2558_CR11","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1109\/TMM.2022.3144890","volume":"25","author":"N Jiang","year":"2022","unstructured":"Jiang N, Sheng B, Li P, Lee T-Y (2022) Photohelper: portrait photographing guidance via deep feature retrieval and fusion. IEEE Trans Multimed 25:2226\u20132238","journal-title":"IEEE Trans Multimed"},{"key":"2558_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2021","unstructured":"Lin X, Sun S, Huang W, Sheng B, Li P, Feng DD (2021) Eapt: efficient attention pyramid transformer for image processing. IEEE Trans Multimed 25:50\u201361","journal-title":"IEEE Trans Multimed"},{"issue":"2","key":"2558_CR13","first-page":"584","volume":"30","author":"L Dai","year":"2024","unstructured":"Dai L, Sheng B, Chen T, Wu Q, Liu R, Cai C, Wu L, Yang D, Hamzah H, Liu Y et al (2024) A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med, Nat Publ Group US New York 30(2):584\u2013594","journal-title":"Nat Med, Nat Publ Group US New York"},{"issue":"1","key":"2558_CR14","first-page":"3242","volume":"12","author":"L Dai","year":"2021","unstructured":"Dai L, Wu L, Li H, Cai C, Wu Q, Kong H, Liu R, Wang X, Hou X, Liu Y et al (2021) A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun, Nat Publ Group UK London 12(1):3242","journal-title":"Nat Commun, Nat Publ Group UK London"},{"key":"2558_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2024.3374068","author":"J Zhu","year":"2024","unstructured":"Zhu J, Chen X, Hu Q, Xiao Y, Wang B, Sheng B, Chen CP (2024) Clustering environment aware learning for active domain adaptation. IEEE Trans Syst Man Cybern Syst. https:\/\/doi.org\/10.1109\/TSMC.2024.3374068","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"2558_CR16","doi-asserted-by":"crossref","unstructured":"Khodabakhsh A, Ramachandra R, Raja K, Wasnik P, Busch C (2018) Fake face detection methods: Can they be generalized? In: IEEE International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1\u20136","DOI":"10.23919\/BIOSIG.2018.8553251"},{"key":"2558_CR17","doi-asserted-by":"crossref","unstructured":"Mo H, Chen B, Luo W (2018) Fake faces identification via convolutional neural network. In: Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security, pp. 43\u201347","DOI":"10.1145\/3206004.3206009"},{"issue":"3","key":"2558_CR18","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TIFS.2014.2300937","volume":"9","author":"G Cao","year":"2014","unstructured":"Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9(3):515\u2013525","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"2","key":"2558_CR19","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1080\/23307706.2022.2033644","volume":"10","author":"S Suratkar","year":"2023","unstructured":"Suratkar S, Bhiungade S, Pitale J, Soni K, Badgujar T, Kazi F (2023) Deep-fake video detection approaches using convolutional-recurrent neural networks. J Control Decis 10(2):198\u2013214","journal-title":"J Control Decis"},{"key":"2558_CR20","doi-asserted-by":"crossref","unstructured":"Yang X, Li Y, Lyu S (2019) Exposing deep fakes using inconsistent head poses. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261\u20138265","DOI":"10.1109\/ICASSP.2019.8683164"},{"issue":"1","key":"2558_CR21","doi-asserted-by":"publisher","first-page":"370","DOI":"10.3390\/app10010370","volume":"10","author":"C-C Hsu","year":"2020","unstructured":"Hsu C-C, Zhuang Y-X, Lee C-Y (2020) Deep fake image detection based on pairwise learning. Appl Sci 10(1):370","journal-title":"Appl Sci"},{"key":"2558_CR22","doi-asserted-by":"crossref","unstructured":"Sohrawardi SJ, Chintha A, Thai B, Seng S, Hickerson A, Ptucha R, Wright M (2019) Poster: Towards robust open-world detection of deepfakes. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, pp. 2613\u20132615","DOI":"10.1145\/3319535.3363269"},{"key":"2558_CR23","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":"2558_CR24","doi-asserted-by":"crossref","unstructured":"Tariq S, Lee S, Kim H, Shin Y, Woo SS (2019) Gan is a friend or foe? a framework to detect various fake face images. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, pp. 1296\u20131303","DOI":"10.1145\/3297280.3297410"},{"key":"2558_CR25","doi-asserted-by":"crossref","unstructured":"Dang H, Liu F, Stehouwer J, Liu X, Jain AK (2020) On the detection of digital face manipulation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5781\u20135790","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"2558_CR26","doi-asserted-by":"crossref","unstructured":"Liu Z, Qi X, Torr PH (2020) Global texture enhancement for fake face detection in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8060\u20138069","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"2558_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2021.103170","volume":"204","author":"Z Guo","year":"2021","unstructured":"Guo Z, Yang G, Chen J, Sun X (2021) Fake face detection via adaptive manipulation traces extraction network. Comput Vis Image Underst 204:103170","journal-title":"Comput Vis Image Underst"},{"key":"2558_CR28","doi-asserted-by":"crossref","unstructured":"Li D, Wang W, Fan H, Dong J (2021) Exploring adversarial fake images on face manifold. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5789\u20135798","DOI":"10.1109\/CVPR46437.2021.00573"},{"issue":"7","key":"2558_CR29","doi-asserted-by":"publisher","first-page":"3749","DOI":"10.1007\/s11760-023-02602-2","volume":"17","author":"A Kumar","year":"2023","unstructured":"Kumar A, Kaushal M, Sharma A (2023) Sam c-gan: a method for removal of face masks from masked faces. SIViP 17(7):3749\u20133757","journal-title":"SIViP"},{"key":"2558_CR30","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/s00530-021-00756-y","volume":"27","author":"Z Pan","year":"2021","unstructured":"Pan Z, Ren Y, Zhang X (2021) Low-complexity fake face detection based on forensic similarity. Multimed Syst 27:353\u2013361","journal-title":"Multimed Syst"},{"key":"2558_CR31","doi-asserted-by":"crossref","unstructured":"Wang C, Deng W (2021) Representative forgery mining for fake face detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14923\u201314932","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"2558_CR32","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patrec.2021.03.009","volume":"146","author":"Y Wang","year":"2021","unstructured":"Wang Y, Ding X, Yang Y, Ding L, Ward R, Wang ZJ (2021) Perception matters: Exploring imperceptible and transferable anti-forensics for gan-generated fake face imagery detection. Pattern Recogn Lett 146:15\u201322","journal-title":"Pattern Recogn Lett"},{"key":"2558_CR33","unstructured":"Salman FM, Abu-Naser SS (2022) Classification of real and fake human faces using deep learning"},{"key":"2558_CR34","doi-asserted-by":"publisher","first-page":"881","DOI":"10.7717\/peerj-cs.881","volume":"8","author":"S Suganthi","year":"2022","unstructured":"Suganthi S, Ayoobkhan MUA, Bacanin N, Venkatachalam K, \u0160t\u011bp\u00e1n H, Pavel T et al (2022) Deep learning model for deep fake face recognition and detection. PeerJ Comput Sci 8:881","journal-title":"PeerJ Comput Sci"},{"key":"2558_CR35","doi-asserted-by":"crossref","unstructured":"Huang B, Wang Z, Yang J, Ai J, Zou Q, Wang Q, Ye D (2023) Implicit identity driven deepfake face swapping detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4490\u20134499","DOI":"10.1109\/CVPR52729.2023.00436"},{"issue":"1","key":"2558_CR36","doi-asserted-by":"publisher","first-page":"7422","DOI":"10.1038\/s41598-023-34629-3","volume":"13","author":"R Rafique","year":"2023","unstructured":"Rafique R, Gantassi R, Amin R, Frnda J, Mustapha A, Alshehri AH (2023) Deep fake detection and classification using error-level analysis and deep learning. Sci Rep 13(1):7422","journal-title":"Sci Rep"},{"issue":"8","key":"2558_CR37","doi-asserted-by":"publisher","first-page":"9727","DOI":"10.1007\/s13369-022-07321-3","volume":"48","author":"S Suratkar","year":"2023","unstructured":"Suratkar S, Kazi F (2023) Deep fake video detection using transfer learning approach. Arab J Sci Eng 48(8):9727\u20139737","journal-title":"Arab J Sci Eng"},{"key":"2558_CR38","doi-asserted-by":"crossref","unstructured":"Zhu X, Wang H, Fei H, Lei Z, Li SZ (2021) Face forgery detection by 3d decomposition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2929\u20132939","DOI":"10.1109\/CVPR46437.2021.00295"},{"issue":"2","key":"2558_CR39","first-page":"505","volume":"12","author":"SK Sharma","year":"2024","unstructured":"Sharma SK, Khan WA, Kumar M, Bali R (2024) Estimation and concealment deep fake detection in images using hybrid lstm. Int J Intell Syst Appl Eng 12(2):505\u2013522","journal-title":"Int J Intell Syst Appl Eng"},{"issue":"6s","key":"2558_CR40","first-page":"840","volume":"12","author":"S Pant","year":"2024","unstructured":"Pant S, Gosavi C, Barekar S (2024) Deep fake detection using lstm and survey of deep fake creation technologies. Int J Intell Syst Appl Eng 12(6s):840\u2013845","journal-title":"Int J Intell Syst Appl Eng"},{"key":"2558_CR41","doi-asserted-by":"crossref","unstructured":"Lu Y, Ebrahimi T (2024) Towards the detection of ai-synthesized human face images. arXiv preprint arXiv:2402.08750","DOI":"10.1109\/ICIP51287.2024.10647451"},{"issue":"5","key":"2558_CR42","doi-asserted-by":"publisher","first-page":"2149","DOI":"10.3390\/app14052149","volume":"14","author":"T Walczyna","year":"2024","unstructured":"Walczyna T, Piotrowski Z (2024) Fast fake: Easy-to-train face swap model. Appl Sci 14(5):2149","journal-title":"Appl Sci"},{"issue":"1","key":"2558_CR43","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/computers13010031","volume":"13","author":"MA Arshed","year":"2024","unstructured":"Arshed MA, Mumtaz S, Ibrahim M, Dewi C, Tanveer M, Ahmed S (2024) Multiclass ai-generated deepfake face detection using patch-wise deep learning model. Computers 13(1):31","journal-title":"Computers"},{"issue":"11s","key":"2558_CR44","first-page":"40","volume":"12","author":"K Vyas","year":"2024","unstructured":"Vyas K, Pareek P, Jayaswal R, Patil S (2024) Analysing the landscape of deep fake detection: A survey. Int J Intell Syst Appl Eng 12(11s):40\u201355","journal-title":"Int J Intell Syst Appl Eng"},{"key":"2558_CR45","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-18664-4","author":"T Aieprasert","year":"2024","unstructured":"Aieprasert T, Mahdlang Y, Pansiri C, Sae-Bae N, Khomkham B (2024) Transferability of cnn models for gan-generated face detection. Multim Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-18664-4","journal-title":"Multim Tools Appl"},{"issue":"3","key":"2558_CR46","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/s11196-023-10079-y","volume":"37","author":"R Gramigna","year":"2024","unstructured":"Gramigna R (2024) Preserving anonymity: Deep-fake as an identity-protection device and as a digital camouflage. Int J Semiot Law-Revue Int de S\u00e9miotique Jurid 37(3):729\u201351","journal-title":"Int J Semiot Law-Revue Int de S\u00e9miotique Jurid"},{"key":"2558_CR47","unstructured":"University C (2019) Real and fake face detection. https:\/\/www.kaggle.com\/datasets\/ciplab\/real-and-fake-face-detection (accessed)"},{"issue":"2","key":"2558_CR48","doi-asserted-by":"publisher","first-page":"935","DOI":"10.3390\/app13020935","volume":"13","author":"BA Kumar","year":"2023","unstructured":"Kumar BA, Bansal M (2023) Face mask detection on photo and real-time video images using Caffe-MobileNetV2 transfer learning. Appl Sci 13(2):935","journal-title":"Appl Sci"},{"key":"2558_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123179","volume":"246","author":"BA Kumar","year":"2024","unstructured":"Kumar BA, Misra NK (2024) Masked face age and gender identification using caffe-modified mobilenetv2 on photo and real-time video images by transfer learning and deep learning techniques. Expert Syst Appl, Elsevier 246:123179","journal-title":"Expert Syst Appl, Elsevier"},{"key":"2558_CR50","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"},{"key":"2558_CR51","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"2558_CR52","unstructured":"ImageNet http:\/\/www.image-net.org"},{"key":"2558_CR53","unstructured":"Applications K https:\/\/keras.io\/api\/applications\/"},{"key":"2558_CR54","first-page":"27","volume":"6","author":"C Room","year":"2019","unstructured":"Room C (2019) Confusion matrix. Mach Learn 6:27","journal-title":"Mach Learn"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02558-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02558-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02558-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T04:35:33Z","timestamp":1757133333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02558-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":54,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2558"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02558-4","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1038\/s41598-026-61247-6","asserted-by":"object"},{"id-type":"doi","id":"10.1038\/s41598-026-50603-1","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]},"assertion":[{"value":"19 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Declared no Conflict of interest by authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not relevant.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not relevant.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not relevant.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}