{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:37:21Z","timestamp":1772159841641,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["13S-0206-200273"],"award-info":[{"award-number":["13S-0206-200273"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Rel."],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1109\/tr.2023.3298685","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T13:49:43Z","timestamp":1692020983000},"page":"1367-1382","source":"Crossref","is-referenced-by-count":8,"title":["Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6738-2352","authenticated-orcid":false,"given":"Shawqi","family":"Al-Maliki","sequence":"first","affiliation":[{"name":"Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8141-6793","authenticated-orcid":false,"given":"Faissal El","family":"Bouanani","sequence":"additional","affiliation":[{"name":"ENSIAS, Mohammed V University in Rabat, Rabat, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0931-9275","authenticated-orcid":false,"given":"Kashif","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3261-7588","authenticated-orcid":false,"given":"Mohamed","family":"Abdallah","sequence":"additional","affiliation":[{"name":"Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9528-0863","authenticated-orcid":false,"given":"Dinh Thai","family":"Hoang","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-7416","authenticated-orcid":false,"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0903-1204","authenticated-orcid":false,"given":"Ala","family":"Al-Fuqaha","sequence":"additional","affiliation":[{"name":"Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Krizhevsky","year":"2012"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10073-7"},{"key":"ref4","article-title":"Adversarial attacks and defenses for social network text processing applications: Techniques, challenges and future research directions","author":"Alsmadi","year":"2021"},{"key":"ref5","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"ref6","article-title":"Adversarial examples are not bugs, they are features","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","volume":"32","author":"Ilyas","year":"2019"},{"key":"ref7","article-title":"Intriguing properties of neural networks","author":"Szegedy","year":"2013"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2022.3161138"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2020.3032744"},{"key":"ref10","article-title":"PixelDefend: Leveraging generative models to understand and defend against adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song","year":"2018"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63387-9_5"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00044"},{"key":"ref14","first-page":"5505","article-title":"DVERGE: Diversifying vulnerabilities for enhanced robust generation of ensembles","volume-title":"Proc. Int. Conf.. Neural Inf. Process. Syst.","author":"Yang","year":"2020"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3385003.3410925"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2022.3171420"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref18","first-page":"416","article-title":"On-line algorithms versus off-line algorithms: How much is it worth to know the future","volume-title":"Proc. IFIP Congr.","author":"Karp","year":"1992"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1561\/9781680832136"},{"key":"ref20","article-title":"Thermometer encoding: One hot way to resist adversarial examples","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Buckman","year":"2018"},{"key":"ref21","article-title":"Defense-GAN: Protecting classifiers against adversarial attacks using generative models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Samangouei","year":"2018"},{"key":"ref22","article-title":"Stochastic activation pruning for robust adversarial defense","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dhillon","year":"2018"},{"key":"ref23","article-title":"Early methods for detecting adversarial images","author":"Hendrycks","year":"2016"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref25","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cohen","year":"2019"},{"key":"ref26","first-page":"10693","article-title":"Randomized smoothing of all shapes and sizes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang","year":"2020"},{"key":"ref27","first-page":"21945","article-title":"Denoised smoothing: A provable defense for pretrained classifiers","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Salman","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01471"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00044"},{"key":"ref30","first-page":"4970","article-title":"Improving adversarial robustness via promoting ensemble diversity","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pang","year":"2019"},{"key":"ref31","article-title":"Improving adversarial robustness of ensembles with diversity training","author":"Kariyappa","year":"2019"},{"key":"ref32","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","year":"2018"},{"key":"ref33","first-page":"1633","article-title":"On adaptive attacks to adversarial example defenses","volume-title":"Proc. Adv. Neural Inform. Process. Syst.","author":"Tramer","year":"2020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00126"},{"key":"ref36","first-page":"21692","article-title":"Detecting adversarial examples is (nearly) as hard as classifying them","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tramr","year":"2022"},{"key":"ref37","article-title":"Robustness may be at odds with accuracy","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tsipras","year":"2018"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1249"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63387-9_1"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417065"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref42","article-title":"Skip connections matter: On the transferability of adversarial examples generated with ResNets","author":"Wu","year":"2020"}],"container-title":["IEEE Transactions on Reliability"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/24\/10339143\/10216927.pdf?arnumber=10216927","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T14:11:02Z","timestamp":1709302262000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10216927\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":42,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tr.2023.3298685","relation":{"is-supplemented-by":[{"id-type":"doi","id":"10.36227\/techrxiv.17088941","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.17088941.v2","asserted-by":"object"}],"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.17088941.v2","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.17088941","asserted-by":"object"}]},"ISSN":["0018-9529","1558-1721"],"issn-type":[{"value":"0018-9529","type":"print"},{"value":"1558-1721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12]]}}}