{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T08:43:40Z","timestamp":1780994620481,"version":"3.54.1"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825226","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"7041-7075","source":"Crossref","is-referenced-by-count":2,"title":["Fool \u2019Em All \u2014 Fool-X: A Powerful &amp; Fast Method for Generating Effective Adversarial Images"],"prefix":"10.1109","author":[{"given":"Samer Y.","family":"Khamaiseh","sequence":"first","affiliation":[{"name":"Miami University,Dept. of Computer Science &#x0026; Software Eng.,Ohio,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mathew","family":"Mancino","sequence":"additional","affiliation":[{"name":"CACI International,New Jersey,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deirdre","family":"Jost","sequence":"additional","affiliation":[{"name":"Miami University,Dept. of Computer Science &#x0026; Software Eng.,Oxford,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Al-Alaj","sequence":"additional","affiliation":[{"name":"Virginia Wesleyan University,Department of Computer Science,Virginia,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Bagagem","sequence":"additional","affiliation":[{"name":"Boise State University,Deptartment of Computer Science,Idaho,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edoardo","family":"Serra","sequence":"additional","affiliation":[{"name":"Boise State University,Department of Computer Science,Boise,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref2","article-title":"Safe, multi-agent, reinforcement learning for autonomous driving","author":"Shalev-Shwartz","year":"2016"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC48688.2020.0-157"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ITIA50152.2020.9312310"},{"issue":"6","key":"ref5","article-title":"The robustness of detecting known and unknown ddos saturation attacks in sdn via the integration of supervised and semi-supervised classifiers","volume-title":"Future Internet","volume":"14","author":"Khamaiseh","year":"2022"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI.2016.0116"},{"key":"ref7","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2013"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref9","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3208131"},{"key":"ref11","article-title":"Adversarial machine learning at scale","author":"Kurakin","year":"2016"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140448"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332"},{"key":"ref17","first-page":"2196","article-title":"Minimally distorted adversarial examples with a fast adaptive boundary attack","volume-title":"International Conference on Machine Learning","author":"Croce"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC57700.2023.00087"},{"key":"ref19","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Croce"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01468"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3547330"},{"key":"ref22","first-page":"6808","article-title":"Wasserstein adversarial examples via projected sinkhorn iterations","volume-title":"International Conference on Machine Learning","author":"Wong"},{"key":"ref23","article-title":"Robustness assessment for adversarial machine learning: Problems, solutions and a survey of current neural networks and defenses","author":"Vargas","year":"2019"},{"key":"ref24","article-title":"Accurate, reliable and fast robustness evaluation","volume":"32","author":"Brendel","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref25","first-page":"20 052","article-title":"Fast minimum-norm adversarial attacks through adaptive norm constraints","volume":"34","author":"Pintor","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref27","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2019"},{"key":"ref28","article-title":"Adversarial distributional training for robust deep learning","author":"Dong","year":"2020"},{"key":"ref29","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"Zhang","year":"2019"},{"key":"ref30","article-title":"Geometry-aware instance-reweighted adversarial training","author":"Zhang","year":"2021"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01304"},{"key":"ref32","article-title":"Cat: Customized adversarial training for improved robustness","author":"Cheng","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3394885.3431542"},{"key":"ref34","article-title":"Adversarial weight perturbation helps robust generalization","author":"Wu","year":"2020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20065-6_18"},{"key":"ref36","article-title":"You only propagate once: Accelerating adversarial training via maximal principle","author":"Zhang","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/520"},{"key":"ref38","article-title":"Attacks which do not kill training make adversarial learning stronger","author":"Zhang","year":"2020"},{"key":"ref39","article-title":"The space of transferable adversarial examples","author":"Tram\u00e8r","year":"2017"},{"key":"ref40","article-title":"Delving into transferable adversarial examples and black-box attacks","author":"Liu","year":"2016"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","location":"Washington, DC, USA","start":{"date-parts":[[2024,12,15]]},"end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825226.pdf?arnumber=10825226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T07:46:03Z","timestamp":1737099963000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825226\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825226","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}