{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:06:03Z","timestamp":1776888363625,"version":"3.51.2"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Center for Applied Research in Artificial Intelligence"},{"name":"DAPA and ADD","award":["UD190031RD"],"award-info":[{"award-number":["UD190031RD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tnnls.2023.3264256","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T17:30:07Z","timestamp":1681493407000},"page":"12665-12677","source":"Crossref","is-referenced-by-count":4,"title":["Advancing Adversarial Training by Injecting Booster Signal"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6626-5683","authenticated-orcid":false,"given":"Hong Joo","family":"Lee","sequence":"first","affiliation":[{"name":"Image and Video Systems Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-2080","authenticated-orcid":false,"given":"Youngjoon","family":"Yu","sequence":"additional","affiliation":[{"name":"Image and Video Systems Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5306-6853","authenticated-orcid":false,"given":"Yong Man","family":"Ro","sequence":"additional","affiliation":[{"name":"Image and Video Systems Laboratory, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00487"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2900709"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054476"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3115626"},{"key":"ref7","first-page":"5758","article-title":"Lip to speech synthesis with visual context attentional GAN","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","author":"Kim"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462506"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.668"},{"key":"ref10","first-page":"2232","article-title":"A tensorized transformer for language modeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ma"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1452"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3127960"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.432"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3043002"},{"key":"ref16","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref17","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2017","journal-title":"arXiv:1706.06083"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref19","article-title":"Keeping the bad guys out: Protecting and vaccinating deep learning with JPEG compression","author":"Das","year":"2017","journal-title":"arXiv:1705.02900"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00095"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00034"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00070"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00143"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00669"},{"key":"ref25","article-title":"Mitigating adversarial effects through randomization","author":"Xie","year":"2017","journal-title":"arXiv:1711.01991"},{"key":"ref26","article-title":"Robust ensemble model training via random layer sampling against adversarial attack","author":"Lee","year":"2020","journal-title":"arXiv:2005.10757"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_23"},{"key":"ref28","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Athalye"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00357"},{"key":"ref30","first-page":"12719","article-title":"Expressive 1-Lipschitz neural networks for robust multiple graph learning against adversarial attacks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Zhao"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.12.024"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3089128"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/591"},{"key":"ref34","article-title":"Geometry-aware instance-reweighted adversarial training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref35","first-page":"11278","article-title":"Attacks which do not kill training make adversarial learning stronger","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref36","article-title":"Improving adversarial robustness requires revisiting misclassified examples","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref37","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","volume":"97","author":"Zhang"},{"key":"ref38","article-title":"Adversarial logit pairing","author":"Kannan","year":"2018","journal-title":"arXiv:1803.06373"},{"key":"ref39","article-title":"Helper-based adversarial training: Reducing excessive margin to achieve a better accuracy vs. robustness trade-off","volume-title":"Proc. ICML Workshop Adversarial Mach. Learn.","author":"Rade"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403084"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI47803.2020.9308597"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO.2019.8902630"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00741"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3032061"},{"key":"ref45","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref46","first-page":"2196","article-title":"Minimally distorted adversarial examples with a fast adaptive boundary attack","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"ref48","article-title":"RobustBench: A standardized adversarial robustness benchmark","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track","author":"Croce"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3062977"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"ref51","article-title":"PixelDefend: Leveraging generative models to understand and defend against adversarial examples","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Song"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11504"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00041"},{"key":"ref54","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"issue":"7","key":"ref55","first-page":"3","article-title":"Tiny ImageNet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref57","article-title":"Fast is better than free: Revisiting adversarial training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wong"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref59","article-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","author":"Xu","year":"2017","journal-title":"arXiv:1704.01155"},{"key":"ref60","article-title":"Countering adversarial images using input transformations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Guo"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10663876\/10102499.pdf?arnumber=10102499","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T18:16:34Z","timestamp":1725473794000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10102499\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":60,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2023.3264256","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}