{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:20:18Z","timestamp":1778347218782,"version":"3.51.4"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-23-1-0214"],"award-info":[{"award-number":["W911NF-23-1-0214"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-24-2-0133"],"award-info":[{"award-number":["W911NF-24-2-0133"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3544107","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T20:34:32Z","timestamp":1740083672000},"page":"35230-35242","source":"Crossref","is-referenced-by-count":16,"title":["Evaluating Pretrained Deep Learning Models for Image Classification Against Individual and Ensemble Adversarial Attacks"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4870-0232","authenticated-orcid":false,"given":"Mafizur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Computer Science, Prairie View A&#x0026;M University, Prairie View, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0563-3656","authenticated-orcid":false,"given":"Prosenjit","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prairie View A&#x0026;M University, Prairie View, TX, USA"}]},{"given":"Sherri S.","family":"Frizell","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Prairie View A&#x0026;M University, Prairie View, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1577-3359","authenticated-orcid":false,"given":"Lijun","family":"Qian","sequence":"additional","affiliation":[{"name":"CREDIT Center, Prairie View A&#x0026;M University, Prairie View, TX, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CoDIT.2019.8820299"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13850"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9564641"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-0808-6_15"},{"key":"ref6","first-page":"247","article-title":"KENKU: Towards efficient and stealthy black-box adversarial attacks against ASR systems","volume-title":"Proc. 32nd USENIX Secur. Symp. (USENIX Secur.)","author":"Wu"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33012253"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/computers9030058"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01161"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2018.2874243"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.12.012"},{"key":"ref14","volume-title":"Zh-plus\/tiny-imagenet \u00b7 Datasets at Hugging Face","year":"2023"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3302504.3311814"},{"key":"ref16","article-title":"Survey of vulnerabilities in large language models revealed by adversarial attacks","author":"Shayegani","year":"2023","journal-title":"arXiv:2310.10844"},{"key":"ref17","first-page":"12288","article-title":"Learning black-box attackers with transferable priors and query feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2017.2783441"},{"key":"ref19","article-title":"Black-box adversarial attack with transferable model-based embedding","author":"Huang","year":"2019","journal-title":"arXiv:1911.07140"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107184"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/GCCE53005.2021.9621775"},{"key":"ref22","article-title":"Transferability in machine learning: From phenomena to black-box attacks using adversarial samples","author":"Papernot","year":"2016","journal-title":"arXiv:1605.07277"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109037"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICICCS56967.2023.10142251"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-44207-0_45"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5220\/0010313705850592"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2022.116747"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.3390\/e25020215"},{"key":"ref29","first-page":"1","article-title":"Ensemble adversarial training: Attacks and defenses","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Tram\u00e8r"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3039295"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01456"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3393072"},{"key":"ref33","article-title":"Graph neural network explanations are fragile","author":"Li","year":"2024","journal-title":"arXiv:2406.03193"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01573"},{"key":"ref35","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2046684.2046692"},{"key":"ref37","article-title":"Adversarial sparse teacher: Defense against distillation-based model stealing attacks using adversarial examples","author":"Yilmaz","year":"2024","journal-title":"arXiv:2403.05181"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/INOCON60754.2024.10511920"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.36.1.48"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2017.2718494"},{"key":"ref41","first-page":"9607","article-title":"Nash equilibria and pitfalls of adversarial training in adversarial robustness games","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Balcan"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3048120"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3127960"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00143"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_28"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3126733"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10896655.pdf?arnumber=10896655","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T18:54:52Z","timestamp":1740768892000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10896655\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3544107","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}