{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:05:31Z","timestamp":1772787931135,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"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"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11042-023-14664-y","type":"journal-article","created":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T08:03:34Z","timestamp":1681200214000},"page":"42173-42205","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A framework for evaluating image obfuscation under deep learning-assisted privacy attacks"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6021-5554","authenticated-orcid":false,"given":"Jimmy","family":"Tekli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bechara","family":"Al Bouna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilbert","family":"Tekli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rapha\u00ebl","family":"Couturier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"key":"14664_CR1","doi-asserted-by":"crossref","unstructured":"Abramian D, Eklund A (2018) Refacing: reconstructing anonymized facial features using GANs COCR, volume. arXiv:1810.06455","DOI":"10.1101\/447102"},{"issue":"12","key":"14664_CR2","doi-asserted-by":"publisher","first-page":"20372041","DOI":"10.1109\/TPAMI.2006.244","volume":"28","author":"T Ahonen","year":"2006","unstructured":"Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. Patt Anal Mach Intell, IEEE Trans 28(12):20372041","journal-title":"Patt Anal Mach Intell, IEEE Trans"},{"key":"14664_CR3","unstructured":"Amos B, Ludwiczuk B, Satyanarayanan M (2016) Openface: a general purpose face recognition library with mobile applications, Tech Rep, CMU Sch Comput Sci, CMU-CS-16-118"},{"key":"14664_CR4","first-page":"1","volume":"17","author":"M Bansal","year":"2021","unstructured":"Bansal M, Kumar M, Sachdeva M, Mittal A (2021) Transfer learning for image classification using VGG19: Caltech-101 image data set. J Ambient Intell Humaniz Comput, 2021 Sep 17:1\u201312","journal-title":"J Ambient Intell Humaniz Comput, 2021 Sep"},{"issue":"7","key":"14664_CR5","doi-asserted-by":"publisher","first-page":"711720","DOI":"10.1109\/34.598228","volume":"19","author":"P Belhumeur","year":"1997","unstructured":"Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. Sherfaces: recognition using class specific linear projection. Patt Anal Mach Intell, IEEE Trans 19(7):711720","journal-title":"Patt Anal Mach Intell, IEEE Trans"},{"key":"14664_CR6","doi-asserted-by":"crossref","unstructured":"Bellare M, Pointcheval D, Rogaway P (2000) Authenticated Key Exchange Secure against Dictionary Attacks. In: Advances in Cryptology \u2014 EUROCRYPT 2000, lecture notes in computer science. Springer, pp 139\u2013155, Berlin","DOI":"10.1007\/3-540-45539-6_11"},{"key":"#cr-split#-14664_CR7.1","unstructured":"Bellare M, Rogaway P (1993) Entity authentication and key distribution, Advances in Cryptology-CRYPTO' 93. In: Stinson DR"},{"key":"#cr-split#-14664_CR7.2","unstructured":"(ed) Lecture notes in computer science, 1993. Springer, pp 232-249, Berlin"},{"key":"14664_CR8","doi-asserted-by":"crossref","unstructured":"Bellare M, Rogaway P (1995) Provably secure session key distribution: The Three Party Case. In: Proceedings of the 27th annual ACM symposium on theory of computing, Las Vegas, pp 57\u201366","DOI":"10.1145\/225058.225084"},{"key":"14664_CR9","unstructured":"Biggio B, Nelson B, Laskov P (2012) Poisoning attacks against support vector machines arXiv:1206.6389"},{"issue":"8","key":"14664_CR10","doi-asserted-by":"publisher","first-page":"3502","DOI":"10.1109\/TIP.2012.2192126","volume":"21","author":"A Foi","year":"2012","unstructured":"Boracchi G, Foi A (2012) Modeling the performance of image restoration from motion blur. Image Process, IEEE Trans 21(8):3502\u20133517","journal-title":"Image Process, IEEE Trans"},{"key":"14664_CR11","doi-asserted-by":"crossref","unstructured":"Caesar H, Bankiti V, Lang AH, Vora S, Liong VE, Xu Q, Krishnan A, Pan Y, Baldan G, Beijbom O (2019) nuscenes: a multimodal dataset for autonomous driving. arXiv:1903.11027","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"14664_CR12","doi-asserted-by":"crossref","unstructured":"Chattopadhyay A, Ruska R, Pfantz L (2021) Determining the robustness of privacy enhancing deID against the reID adversary: an experimental study the 16th international conference on availability. Reliabil Secur","DOI":"10.1145\/3465481.3469210"},{"key":"14664_CR13","unstructured":"Chen L, Papandreou G, Kokkinos I, Murphy K (2016) A L.Yuille, Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected. arXiv:1606.00915"},{"key":"14664_CR14","doi-asserted-by":"crossref","unstructured":"Dargan S, Kumar M, Ayyagari MR et al (2020) A survey of deep learning and its applications: a new paradigm to machine learning arch computat methods eng, vol 27, pp 1071\u20131092","DOI":"10.1007\/s11831-019-09344-w"},{"key":"14664_CR15","doi-asserted-by":"crossref","unstructured":"Do Q, Martini B, Choo K-KR (2018) The role of the adversary model in applied security research. Comput Secur","DOI":"10.1016\/j.cose.2018.12.002"},{"issue":"7","key":"14664_CR16","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.1109\/TIP.2011.2108306","volume":"20","author":"W Dong","year":"2011","unstructured":"Dong W, Zhang L, Shi G, Image deblurring XWu (2011) Superresolution by adaptive sparse domain selection and adaptive regularization. Image Process, IEEE Trans 20(7):1838\u20131857","journal-title":"Image Process, IEEE Trans"},{"key":"14664_CR17","doi-asserted-by":"crossref","unstructured":"Dufaux F, Ebrahimi T (2010) A framework for the validation of privacy protection solutions in video surveillance. In: IEEE international conference on multimedia and expo","DOI":"10.1109\/ICME.2010.5583552"},{"key":"14664_CR18","doi-asserted-by":"crossref","unstructured":"Frome A, Cheung G, Abdulkader A, Zennaro M et al (2009) Large-scale privacy protection in Google Street View IEEE 12th International Conference on Computer Vision ICCV","DOI":"10.1109\/ICCV.2009.5459413"},{"key":"14664_CR19","unstructured":"Garcia D (2016) srez: adversarial super resolution. http:\/\/github.com\/david-gpu\/srez. Accessed 2019"},{"key":"14664_CR20","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples in ICLR"},{"key":"14664_CR21","doi-asserted-by":"crossref","unstructured":"Gopalan R, Taheri S, Turaga P, Chellappa R (2012) A blur-robust descriptor with applications to face recognition. IEEE Trans Patt Anal Mach Intell","DOI":"10.1109\/TPAMI.2012.15"},{"key":"14664_CR22","doi-asserted-by":"crossref","unstructured":"Hao H, G\u00fcera D, Horv\u00e1th J, Reibman AR, Delp EJ (2020) Robustness analysis of face obscuration 2020 15th IEEE international conference on automatic face and gesture recognition","DOI":"10.1109\/FG47880.2020.00021"},{"key":"14664_CR23","unstructured":"Hao H, G\u00fcera D, Reibman AR, Delp EJ (2019) A utility-preserving gan for face obscuration. In: Proceedings of the international conference on machine learning, synthetic realities: deep learning for detecting audio visual fakes workshop"},{"key":"14664_CR24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition CVPR","DOI":"10.1109\/CVPR.2016.90"},{"key":"14664_CR25","doi-asserted-by":"crossref","unstructured":"Hill S, Zhou Z, Saul L, Shacham H (2016) On the in effectiveness of mosaicing and blurring as tools for document redaction PETS","DOI":"10.1515\/popets-2016-0047"},{"key":"14664_CR26","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2019) Squeeze-and-excitation networks, arXiv:1709.01507","DOI":"10.1109\/CVPR.2018.00745"},{"key":"14664_CR27","unstructured":"Jin CB (2018) Semantic-image-inpainting. https:\/\/github.com\/ChengBinJin\/semantic-image-inpainting. Accessed 2019"},{"key":"14664_CR28","unstructured":"Jingzhi L, Lutong H, Ruoyu C, Hua Z, Bing H, Lili W, Xiaochun C (2021) Identity-preserving face anonymization via adaptively facial attributes obfuscation. In: Proceedings of the 29th ACM international conference on multimedia"},{"issue":"6","key":"14664_CR29","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"R Keys","year":"1981","unstructured":"Keys R (1981) Cubic convolution interpolation for digital image processing. Acoust, Speech Signal Process IEEE Trans 29(6):1153\u20131160","journal-title":"Acoust, Speech Signal Process IEEE Trans"},{"key":"14664_CR30","unstructured":"Komkov S, Petiushko A (2019) Advhat: real-world adversarial attack on arcface face id system arXiv:1908.08705"},{"key":"14664_CR31","doi-asserted-by":"crossref","unstructured":"Korshunov P, Melle A, Dugelay J-L, Ebrahimi T (2013) Framework for objective evaluation of privacy filters. In: Proceedings of SPIE, vol 8856, p 12","DOI":"10.1117\/12.2027040"},{"key":"14664_CR32","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images"},{"key":"14664_CR33","unstructured":"Laboratories Cambridge (1994) The database of faces"},{"key":"14664_CR34","doi-asserted-by":"crossref","unstructured":"Lander K, Bruce V, Hill H (2001) Evaluating the effectiveness of pixelation and blurring on masking the identity of familiar faces. Appl Cognit Psychol","DOI":"10.1002\/1099-0720(200101\/02)15:1<101::AID-ACP697>3.0.CO;2-7"},{"key":"14664_CR35","unstructured":"LeCun Y, Cortes C, Burges CJ (1998) The mnist database of handwritten digits"},{"key":"14664_CR36","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham $\\o:left: .\\relax \\special {t4ht=<mfenced separators=\"\" open=\"|\" }\\bgroup \\relax \\special {t4ht=><mrow>}\\bgroup N\\egroup \\egroup \\o:right: .\\relax \\special {t4ht=<\/mrow><\/mfenced>}$ A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2016) Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802","DOI":"10.1109\/CVPR.2017.19"},{"key":"14664_CR37","doi-asserted-by":"crossref","unstructured":"Li Y, Liu S, Yang J, Yang M. -H. (2017) Generative face completion, arXiv:1704.05838","DOI":"10.1109\/CVPR.2017.624"},{"key":"14664_CR38","doi-asserted-by":"crossref","unstructured":"Li Y, Vishwamitra N, Knijnenburg BP, Hu H, Caine K (2017) Effectiveness and users\u2019 experience of obfuscation as a privacy-enhancing technology for sharing photos. In: Proceedings of the ACM on human-computer interaction","DOI":"10.1145\/3134702"},{"key":"14664_CR39","unstructured":"Linwei Y, Binglin L, Noman M, Yang W, Jie L (2018) Privacy-Preserving Age Estimation for Content Rating. In: 2018 IEEE 20th international workshop on multimedia signal processing (MMSP)"},{"key":"14664_CR40","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S (2015) SSD: Single shot multibox detector. arXiv:1512.02325","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"14664_CR41","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.425"},{"key":"14664_CR42","unstructured":"Majumdar S (2016) Image Super Resolution. https:\/\/github.com\/titu1994\/Image-Super-Resolution. Accessed 2019"},{"key":"14664_CR43","unstructured":"McPherson R, Shokri R, Shmatikov V (2016) Defeating image obfuscation with deep learning coRR"},{"key":"14664_CR44","doi-asserted-by":"crossref","unstructured":"Meden B, Rot P, Terh\u00f6rst P, Damer N, Kuijper A, Scheirer WJ, Ross A, Peer P, Struc V (2021) Privacy-enhancing face biometrics: a comprehensive survey. IEEE Trans Inf Forensics Secur","DOI":"10.1109\/TIFS.2021.3096024"},{"key":"14664_CR45","doi-asserted-by":"crossref","unstructured":"Nawaz T, Berg A, Ferryman J, Ahlberg J, Felsberg M (2017) Effective evaluation of privacy protection techniques in visible and thermal imagery. J Electron Imaging","DOI":"10.1117\/1.JEI.26.5.051408"},{"key":"14664_CR46","doi-asserted-by":"crossref","unstructured":"Newton EM, Sweeney L (2005) Preserving privacy by de-identifying face images. In: IEEE transactions on knowledge and data engineering","DOI":"10.1109\/TKDE.2005.32"},{"key":"14664_CR47","doi-asserted-by":"crossref","unstructured":"Newton EM, Sweeney L, Malin B (2005) Preserving privacy by de-identifying face images. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2005.32"},{"key":"14664_CR48","doi-asserted-by":"crossref","unstructured":"Ng H-W, Winkler S (2014) A data-driven approach to cleaning large face datasets. In: IEEE international conference on image processing (ICIP)","DOI":"10.1109\/ICIP.2014.7025068"},{"key":"14664_CR49","unstructured":"Packhauser K, G\u00fcndel S, M\u00fcnster N, Syben C, Christlein V, Maier A (2021) Is Medical Chest X-ray Data Anonymous? arXiv:2103.08562:v1"},{"key":"14664_CR50","doi-asserted-by":"crossref","unstructured":"Punnappurath A, Rajagopalan AN, Taheri S, Chellappa R, Seetharaman G (2015) Face recognition across non-uniform motion blur, illumination, and pose. IEEE Trans Image Process","DOI":"10.1109\/TIP.2015.2412379"},{"key":"14664_CR51","unstructured":"Ra M-R, Govindan R, Ortega A (2013) P3:Toward privacy-preserving photo sharing, NSDI"},{"key":"14664_CR52","doi-asserted-by":"crossref","unstructured":"Rezaeifar S, Voloshynovskiy S (2022) M asgari jirhandeh and v kinakh privacy-preserving image template sharing using contrastive learning entropy","DOI":"10.3390\/e24050643"},{"key":"14664_CR53","doi-asserted-by":"crossref","unstructured":"Ruchaud N, Dugelay JL (2016), Automatic face anonymization in visual data: are we really well protected? Electron Imaging","DOI":"10.2352\/ISSN.2470-1173.2016.15.IPAS-181"},{"key":"14664_CR54","doi-asserted-by":"crossref","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2014) ImageNet large scale visual recognition challenge. arXiv:1409.0575","DOI":"10.1007\/s11263-015-0816-y"},{"key":"14664_CR55","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815\u2013823","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"14664_CR56","unstructured":"Shafahi A, Huang WR, Najibi M, Suciu O, Studer C, Dumitras T, Goldstein T (2018) Poison frogs! targeted clean-label poisoning attacks on neural networks. In: Proc of NeurIPS"},{"key":"14664_CR57","doi-asserted-by":"crossref","unstructured":"Shaheed K, Mao A, Qureshi I, Kumar M, Hussain S, Ullah I, Zhang X (2022) DS-CNN: a pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition. Exp Syst Appl","DOI":"10.1016\/j.eswa.2021.116288"},{"key":"14664_CR58","unstructured":"Shan S, Wenger E, Zhang J, Li H, H Zheng, Zhao BY (2020) Fawkes: Protecting personal privacy against unauthorized deep learning models. arXiv:2002.08327"},{"key":"14664_CR59","doi-asserted-by":"crossref","unstructured":"Shen Z (2016) Deep-semantic-face-deblurring. https:\/\/github.com\/joanshen0508\/Deep-Semantic-Face-Deblurring. Accessed 2019","DOI":"10.1186\/s13638-019-1350-3"},{"key":"14664_CR60","doi-asserted-by":"crossref","unstructured":"Shen Z, Lai W, Xu T, Kautz J, Yang SM (2018) Deep semantic face deblurring CVPR:8260\u20138269","DOI":"10.1109\/CVPR.2018.00862"},{"key":"14664_CR61","doi-asserted-by":"crossref","unstructured":"Tekli J, al Bouna B, Couturier R, Tekli G, al Zein Z, Kamradt M (2019) A framework for evaluating image obfuscation under deep learning-assisted privacy attacks. In: 2019 17th international conference on privacy, security and trust (PST), Fredericton, NB, Canada, 2019, pp 1\u201310","DOI":"10.1109\/PST47121.2019.8949040"},{"key":"14664_CR62","unstructured":"Turk M, Pentland A (1991) Face recognition using eigenfaces. Computer vision and pattern recognition, proceedings CVPR \u201991. IEEE computer society conference"},{"key":"14664_CR63","doi-asserted-by":"crossref","unstructured":"Vu HN, Nguyen MH, Pham C (2022) Masked face recognition with convolutional neural networks and local binary patterns. Appl Intell","DOI":"10.1007\/s10489-021-02728-1"},{"key":"14664_CR64","doi-asserted-by":"crossref","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error measurement to structural similarity. Image Process, IEEE Trans, vol 13","DOI":"10.1109\/TIP.2003.819861"},{"key":"14664_CR65","doi-asserted-by":"crossref","unstructured":"Wang X et al (2017) Chestx-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2097\u20132106","DOI":"10.1109\/CVPR.2017.369"},{"key":"14664_CR66","unstructured":"Wu Z, Wang ZH, Wang Z, Jin H, Wang Z (2020) Privacy-preserving deep action recognition: an adversarial learning framework and a new dataset. arxiv:1906.05675v4"},{"key":"14664_CR67","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks, In Computer Vision and Pattern Recognition (CVPR). In: 2017 IEEE Conference on. IEEE, pp 5987\u20135995","DOI":"10.1109\/CVPR.2017.634"},{"key":"14664_CR68","unstructured":"Yang K, Yau J, Fei-Fei L, Deng J, Russakovsky O (2021) A study of face Obfuscation in ImageNet. arXiv:2103.06191v2"},{"key":"14664_CR69","unstructured":"Yang W, Zhang X, Tian Y, Wang W, Xue JH (2018) Deep learning for single image super-resolution: A brief review. arXiv:1808.03344"},{"key":"14664_CR70","doi-asserted-by":"crossref","unstructured":"Yeh RA, Chen C, Lim TY, Schwing AG, Hasegawa-Johnson M, Do MN (2017) Semantic image inpainting with deep generative models. Proc IEEE Conf Comput Vis Pattern Recognit:5485\u20135493","DOI":"10.1109\/CVPR.2017.728"},{"key":"14664_CR71","doi-asserted-by":"crossref","unstructured":"Yu X, Porikli F (2016) Ultra-resolving face images by discriminative generative networks. Springer Int Pub:318\u2013333","DOI":"10.1007\/978-3-319-46454-1_20"},{"key":"14664_CR72","doi-asserted-by":"crossref","unstructured":"Zhang K, Van Gool L, Timofte R (2020) Deep unfolding network for image super-resolution. IEEE Conf Comput Vision Patt Recognit","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"14664_CR73","doi-asserted-by":"crossref","unstructured":"Zhu J, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. CoRR, vol arXiv:1703.10593","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14664-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14664-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14664-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T10:10:45Z","timestamp":1698315045000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14664-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,11]]},"references-count":74,"journal-issue":{"issue":"27","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["14664"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14664-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,11]]},"assertion":[{"value":"4 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors consent that this paper can be published in case of acceptance.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent for Publication"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}