{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:09:39Z","timestamp":1772042979875,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s10489-021-02846-w","type":"journal-article","created":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T11:02:50Z","timestamp":1636714970000},"page":"9001-9016","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["FATALRead - Fooling visual speech recognition models"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1090-6036","authenticated-orcid":false,"given":"Anup Kumar","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Puneet","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esa","family":"Rahtu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"2846_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11\u201326. https:\/\/doi.org\/10.1016\/j.neucom.2016.12.038. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231216315533","journal-title":"Neurocomputing"},{"key":"2846_CR2","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and Harnessing Adversarial Examples. In: International Conference on Learning Representations, (ICLR). https:\/\/research.google\/pubs\/pub43405\/"},{"key":"2846_CR3","doi-asserted-by":"publisher","unstructured":"Gupta P, Rahtu E (2019) MLAttack: Fooling Semantic Segmentation Networks by Multi-layer Attacks. In: German Conference on Pattern Recognition (GCPR). https:\/\/doi.org\/10.1007\/978-3-030-33676-9_28. Springer, pp 401\u2013413","DOI":"10.1007\/978-3-030-33676-9_28"},{"issue":"4","key":"2846_CR4","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MSP.2020.2985363","volume":"37","author":"A Modas","year":"2020","unstructured":"Modas A, Sanchez-Matilla R, Frossard P, Cavallaro A (2020) Toward robust sensing for autonomous vehicles: An adversarial perspective. IEEE Signal Process Mag 37(4):14\u201323. https:\/\/doi.org\/10.1109\/MSP.2020.2985363","journal-title":"IEEE Signal Process Mag"},{"issue":"6","key":"2846_CR5","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s11263-019-01160-w","volume":"127","author":"G Goswami","year":"2019","unstructured":"Goswami G, Agarwal A, Ratha N, Singh R, Vatsa M (2019) Detecting and mitigating adversarial perturbations for robust face recognition. Int J Comput Vis 127(6):719\u2013742. https:\/\/doi.org\/10.1007\/s11263-019-01160-w","journal-title":"Int J Comput Vis"},{"key":"2846_CR6","doi-asserted-by":"publisher","first-page":"104021","DOI":"10.1016\/j.engappai.2020.104021","volume":"96","author":"J Garci\u0301a","year":"2020","unstructured":"Garci\u0301a J, Majadas R, Ferna\u0301ndez F (2020) Learning adversarial attack policies through multi-objective reinforcement learning. Eng Appl Artif Intell 96:104021. https:\/\/doi.org\/10.1016\/j.engappai.2020.104021. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197620303043","journal-title":"Eng Appl Artif Intell"},{"key":"2846_CR7","doi-asserted-by":"publisher","first-page":"104085","DOI":"10.1016\/j.engappai.2020.104085","volume":"97","author":"X Sun","year":"2021","unstructured":"Sun X, Sun S (2021) Adversarial robustness and attacks for multi-view deep models. Eng Appl Artif Intell 97:104085. https:\/\/doi.org\/10.1016\/j.engappai.2020.104085. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197620303419","journal-title":"Eng Appl Artif Intell"},{"key":"2846_CR8","doi-asserted-by":"publisher","first-page":"103641","DOI":"10.1016\/j.engappai.2020.103641","volume":"92","author":"J Xu","year":"2020","unstructured":"Xu J, Du Q (2020) TextTricker: Loss-based and gradient-based adversarial attacks on text classification models. Eng Appl Artif Intell 92:103641. https:\/\/doi.org\/10.1016\/j.engappai.2020.103641. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0952197620300956","journal-title":"Eng Appl Artif Intell"},{"key":"2846_CR9","doi-asserted-by":"publisher","unstructured":"Marino DL, Wickramasinghe CS, Manic M (2018) An adversarial approach for explainable AI in intrusion detection systems. In: (IECON) Annual Conference of the IEEE Industrial Electronics Society. https:\/\/doi.org\/10.1109\/IECON.2018.8591457. IEEE, pp 3237\u20133243","DOI":"10.1109\/IECON.2018.8591457"},{"issue":"9","key":"2846_CR10","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan X, He P, Zhu Q, Li X (2019) Adversarial examples: Attacks and defenses for deep learning. IEEE Trans Neural Netw Learn Syst 30(9):2805\u20132824. https:\/\/doi.org\/10.1109\/TNNLS.2018.2886017","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2846_CR11","doi-asserted-by":"publisher","unstructured":"Ephrat A, Halperin T, Peleg Shmuel (2017) Improved speech reconstruction from silent video. In: International Conference on Computer Vision Workshops (ICCV-W). https:\/\/doi.org\/10.1109\/ICCVW.2017.61. IEEE, pp 455\u2013462","DOI":"10.1109\/ICCVW.2017.61"},{"key":"2846_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.imavis.2018.07.002","volume":"78","author":"A Fernandez-Lopez","year":"2018","unstructured":"Fernandez-Lopez A, Sukno FM (2018) Survey on automatic lip-reading in the era of deep learning. Image Vis Comput 78:53\u201372. https:\/\/doi.org\/10.1016\/j.imavis.2018.07.002","journal-title":"Image Vis Comput"},{"key":"2846_CR13","doi-asserted-by":"publisher","first-page":"55354","DOI":"10.1109\/ACCESS.2020.2982359","volume":"8","author":"M Ezz","year":"2020","unstructured":"Ezz M, Mostafa AM, Nasr AA (2020) A silent password recognition framework based on lip analysis. IEEE Access 8:55354\u201355371. https:\/\/doi.org\/10.1109\/ACCESS.2020.2982359","journal-title":"IEEE Access"},{"key":"2846_CR14","doi-asserted-by":"publisher","unstructured":"Chung JS, Senior A, Vinyals O, Zisserman A (2017) Lip reading sentences in the wild. In: Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2017.367. IEEE, pp 3444\u20133453","DOI":"10.1109\/CVPR.2017.367"},{"key":"2846_CR15","doi-asserted-by":"publisher","unstructured":"Adeel A, Gogate M, Hussain A, Whitmer WM (2019) Lip-reading driven deep learning approach for speech enhancement. IEEE Trans Emerg Top Comput Intell:1\u201310. https:\/\/doi.org\/10.1109\/TETCI.2019.2917039","DOI":"10.1109\/TETCI.2019.2917039"},{"issue":"4","key":"2846_CR16","doi-asserted-by":"publisher","first-page":"112:1","DOI":"10.1145\/3197517.3201357","volume":"37","author":"A Ephrat","year":"2018","unstructured":"Ephrat A, Mosseri I, Lang O, Dekel T, Wilson K, Hassidim A, Freeman WT, Rubinstein M (2018) Looking to listen at the cocktail party: a speaker-independent audio-visual model for speech separation. ACM Trans Graph 37(4):112:1\u2013112:11. https:\/\/doi.org\/10.1145\/3197517.3201357","journal-title":"ACM Trans Graph"},{"key":"2846_CR17","doi-asserted-by":"crossref","unstructured":"Rothkrantz L (2017) Lip-reading by surveillance cameras. In: Smart City Symposium Prague (SCSP). IEEE, pp 1\u20136","DOI":"10.1109\/SCSP.2017.7973348"},{"key":"2846_CR18","doi-asserted-by":"crossref","unstructured":"Xu W, Evans D, Qi Y (2018) Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. In: Network and Distributed Systems Security Symposium (NDSS). https:\/\/wp.internetsociety.org\/ndss\/wp-content\/uploads\/sites\/25\/2018\/02\/ndss2018_03A-4_Xu_paper.pdf","DOI":"10.14722\/ndss.2018.23198"},{"key":"2846_CR19","unstructured":"Dziugaite GK, Ghahramani Z, Roy DM (2016) A study of the effect of JPG compression on adversarial images. arXiv:https:\/\/arxiv.org\/abs\/1608.00853"},{"key":"2846_CR20","unstructured":"Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow IJ, Fergus R (2014) Intriguing properties of neural networks. In: Bengio Y, LeCun Yx (eds) International conference on learning representations, ICLR. https:\/\/research.google\/pubs\/pub42503.pdf"},{"key":"2846_CR21","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow I, Bengio S (2017) Adversarial examples in the physical world. In: International Conference on Learning Representations (ICLR). https:\/\/openreview.net\/forum?id=HJGU3Rodl","DOI":"10.1201\/9781351251389-8"},{"key":"2846_CR22","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2018) Towards deep learning models resistant to adversarial attacks. In: International conference on learning representations, ICLR. https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"key":"2846_CR23","doi-asserted-by":"publisher","unstructured":"Moosavi-Dezfooli S, Fawzi A, Frossard P (2016) Deepfool: A simple and accurate method to fool deep neural networks. In: IEEE conference on computer vision and pattern recognition, CVPR. https:\/\/doi.org\/10.1109\/CVPR.2016.282. IEEE Computer Society, pp 2574\u20132582","DOI":"10.1109\/CVPR.2016.282"},{"key":"2846_CR24","doi-asserted-by":"publisher","unstructured":"Moosavi-Dezfooli S, Fawzi A, Fawzi O, Frossard Pa (2017) Universal adversarial perturbations. In: IEEE conference on computer vision and pattern recognition, CVPR. https:\/\/doi.org\/10.1109\/CVPR.2017.17. IEEE Computer Society, pp 86\u201394","DOI":"10.1109\/CVPR.2017.17"},{"key":"2846_CR25","doi-asserted-by":"publisher","unstructured":"Carlini N, Wagner D (2017) Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy (SP). https:\/\/doi.org\/10.1109\/SP.2017.49. IEEE, pp 39\u201357","DOI":"10.1109\/SP.2017.49"},{"key":"2846_CR26","doi-asserted-by":"publisher","unstructured":"Papernot N, McDaniel P, Wu X, Jha S, Swami A (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: IEEE Symposium on Security and Privacy (SP). https:\/\/doi.org\/10.1109\/SP.2016.41. IEEE, pp 582\u2013597","DOI":"10.1109\/SP.2016.41"},{"key":"2846_CR27","doi-asserted-by":"publisher","unstructured":"Wei X, Zhu J, Yuan S, Su H (2019) Sparse adversarial perturbations for videos. In: Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018973, vol 33. AAAI Press, pp 8973\u20138980","DOI":"10.1609\/aaai.v33i01.33018973"},{"key":"2846_CR28","unstructured":"Inkawhich N, Inkawhich M, Chen Y, Li H (2018) Adversarial attacks for optical flow-based action recognition classifiers. arXiv:https:\/\/arxiv.org\/abs\/1811.11875"},{"key":"2846_CR29","doi-asserted-by":"crossref","unstructured":"Chen Z, Xie L, Pang S, He Y, Tian Q (2021) Appending adversarial frames for universal video attack","DOI":"10.1109\/WACV48630.2021.00324"},{"key":"2846_CR30","doi-asserted-by":"publisher","unstructured":"Zajac M, Zo\u0142na K, Rostamzadeh N, Pinheiro PO (2019) Adversarial framing for image and video classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v33i01.330110077, vol 33. AAAI Press, pp 10077\u201310078","DOI":"10.1609\/aaai.v33i01.330110077"},{"key":"2846_CR31","doi-asserted-by":"crossref","unstructured":"Pony R, Naeh I, Mannor S (2020) Over-the-air adversarial flickering attacks against video recognition networks. arXiv:https:\/\/arxiv.org\/abs\/2002.05123","DOI":"10.1109\/CVPR46437.2021.00058"},{"key":"2846_CR32","doi-asserted-by":"publisher","first-page":"204518","DOI":"10.1109\/ACCESS.2020.3036865","volume":"8","author":"M Hao","year":"2020","unstructured":"Hao M, Mamut M, Yadikar N, Aysa A, Ubul K (2020) A survey of research on lipreading technology. IEEE Access 8:204518\u2013204544. https:\/\/doi.org\/10.1109\/ACCESS.2020.3036865","journal-title":"IEEE Access"},{"issue":"2","key":"2846_CR33","doi-asserted-by":"publisher","first-page":"159","DOI":"10.5566\/ias.1859","volume":"37","author":"F Vakhshiteh","year":"2018","unstructured":"Vakhshiteh F, Almasganj F, Nickabadi A (2018) Lip-reading via deep neural networks using hybrid visual features. Image Anal Stereol 37(2):159\u2013171. https:\/\/doi.org\/10.5566\/ias.1859. https:\/\/www.ias-iss.org\/ojs\/IAS\/article\/view\/1859","journal-title":"Image Anal Stereol"},{"key":"2846_CR34","doi-asserted-by":"publisher","unstructured":"Petridis S, Pantic M (2016) Deep complementary bottleneck features for visual speech recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https:\/\/doi.org\/10.1109\/ICASSP.2016.7472088, pp 2304\u20132308","DOI":"10.1109\/ICASSP.2016.7472088"},{"key":"2846_CR35","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo J (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338. https:\/\/doi.org\/10.1016\/j.neucom.2019.01.078","journal-title":"Neurocomputing"},{"key":"2846_CR36","doi-asserted-by":"crossref","unstructured":"Stafylakis T, Tzimiropoulos G (2017) Combining residual networks with LSTMs for lipreading. In: International Speech Communication Association (INTERSPEECH). https:\/\/www.isca-speech.org\/archive\/Interspeech_2017\/abstracts\/0085.html, pp 3652\u20133656","DOI":"10.21437\/Interspeech.2017-85"},{"key":"2846_CR37","doi-asserted-by":"publisher","unstructured":"Petridis S, Stafylakis T, Ma P, Cai F, Tzimiropoulos G, Pantic M (2018) End-to-end audiovisual speech recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https:\/\/doi.org\/10.1109\/ICASSP.2018.8461326. IEEE, pp 6548\u20136552","DOI":"10.1109\/ICASSP.2018.8461326"},{"key":"2846_CR38","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems (NIPS). https:\/\/papers.nips.cc\/paper\/7181-attention-is-all-you-need, pp 5998\u20136008"},{"key":"2846_CR39","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:https:\/\/arxiv.org\/abs\/1807.00458"},{"key":"2846_CR40","doi-asserted-by":"publisher","unstructured":"Martinez B, Ma P, Petridis S, Pantic M (2020) Lipreading using temporal convolutional networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053841. IEEE, pp 6319\u20136323","DOI":"10.1109\/ICASSP40776.2020.9053841"},{"key":"2846_CR41","unstructured":"Assael YM, Shillingford B, Whiteson S, de Freitas N (2016) Lipnet: sentence-level lipreading. arXiv:https:\/\/arxiv.org\/abs\/1611.01599"},{"key":"2846_CR42","first-page":"120","volume":"25","author":"G Bradski","year":"2000","unstructured":"Bradski G (2000) The OpenCV Library. Dr. Dobb\u2019s J Softw Tools 25:120\u2013125","journal-title":"Dr. Dobb\u2019s J Softw Tools"},{"key":"2846_CR43","unstructured":"Riba E, Fathollahi M, Chaney W, Rublee E, Bradski G (2018) Torchgeometry: when PyTorch meets geometry. https:\/\/drive.google.com\/file\/d\/1xiao1Xj9WzjJ08YY_nYwsthE-wxfyfhG\/view?usp=sharing"},{"key":"2846_CR44","doi-asserted-by":"publisher","unstructured":"Riba E, Mishkin D, Ponsa D, Rublee E, Bradski G (2020) Kornia: an Open Source Differentiable Computer Vision Library for PyTorch. In: IEEE Winter Conference on Applications of Computer Vision (WACV). https:\/\/doi.org\/10.1109\/WACV45572.2020.9093363, pp 3674\u20133683","DOI":"10.1109\/WACV45572.2020.9093363"},{"key":"2846_CR45","doi-asserted-by":"publisher","unstructured":"Chung JS, Zisserman A (2016) Lip reading in the wild. In: Asian Conference on Computer Vision (ACCV). https:\/\/doi.org\/10.1007\/978-3-319-54184-6_6. Springer, pp 87\u2013103","DOI":"10.1007\/978-3-319-54184-6_6"},{"key":"2846_CR46","doi-asserted-by":"publisher","unstructured":"Graese A, Rozsa A, Boult TE (2016) Assessing threat of adversarial examples on deep neural networks. In: IEEE International Conference on Machine Learning and Applications (ICMLA). https:\/\/doi.org\/10.1109\/ICMLA.2016.0020. IEEE, pp 69\u201374","DOI":"10.1109\/ICMLA.2016.0020"},{"key":"2846_CR47","unstructured":"Guo C, Rana M, Cisse M, van der Maaten L (2018) Countering adversarial images using input transformations. In: International Conference on Learning Representations (ICLR)"},{"key":"2846_CR48","doi-asserted-by":"crossref","unstructured":"Gupta P, Rahtu E (2019) CIIDefence: Defeating adversarial attacks by fusing class-specific image inpainting and image denoising. In: IEEE International Conference on Computer Vision (ICCV). https:\/\/openreview.net\/forum?id=SyJ7ClWCb, pp 6708\u20136717","DOI":"10.1109\/ICCV.2019.00681"},{"issue":"5-6","key":"2846_CR49","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5-6):602\u2013610. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608005001206","journal-title":"Neural Netw"},{"key":"2846_CR50","doi-asserted-by":"publisher","unstructured":"Graves A, Fern\u00e1ndez S, Schmidhuber J (2005) Bidirectional lstm networks for improved phoneme classification and recognition. In: International Conference on Artificial Neural Networks (ICANN). https:\/\/doi.org\/10.1007\/11550907_126. Springer, pp 799\u2013804","DOI":"10.1007\/11550907_126"},{"key":"2846_CR51","doi-asserted-by":"publisher","unstructured":"Hayes J, Danezis G (2018) Learning universal adversarial perturbations with generative models. In: IEEE security and privacy workshops, SP workshops. https:\/\/doi.org\/10.1109\/SPW.2018.00015. IEEE Computer Society, pp 43\u201349","DOI":"10.1109\/SPW.2018.00015"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02846-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02846-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02846-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T09:21:17Z","timestamp":1653902477000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02846-w"}},"subtitle":["Put words on Lips"],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":51,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["2846"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02846-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,12]]},"assertion":[{"value":"14 September 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2021","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflicts of interest\/Competing interests"}}]}}