{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:45:50Z","timestamp":1769024750056,"version":"3.49.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001665","name":"European","doi-asserted-by":"publisher","award":["CHIST-ERA ID_IOT"],"award-info":[{"award-number":["CHIST-ERA ID_IOT"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP140101969"],"award-info":[{"award-number":["DP140101969"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ANR-AID Chaire SAIDA"},{"DOI":"10.13039\/501100001691","name":"JSPS Kakenhi Kiban (B) Research","doi-asserted-by":"publisher","award":["18H03296"],"award-info":[{"award-number":["18H03296"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Serbian National Project","award":["OI174023"],"award-info":[{"award-number":["OI174023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tifs.2020.3023274","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T20:27:03Z","timestamp":1599769623000},"page":"854-865","source":"Crossref","is-referenced-by-count":22,"title":["High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0204-0930","authenticated-orcid":false,"given":"Laurent","family":"Amsaleg","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3769-3811","authenticated-orcid":false,"given":"James","family":"Bailey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8789-7302","authenticated-orcid":false,"given":"Amelie","family":"Barbe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0885-0643","authenticated-orcid":false,"given":"Sarah M.","family":"Erfani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1565-765X","authenticated-orcid":false,"given":"Teddy","family":"Furon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8486-8015","authenticated-orcid":false,"given":"Michael E.","family":"Houle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2225-7803","authenticated-orcid":false,"given":"Milos","family":"Radovanovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7275-750X","authenticated-orcid":false,"given":"Xuan Vinh","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.21"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s10687-007-0048-9"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014536"},{"key":"ref32","article-title":"Are adversarial examples inevitable?","author":"shafahi","year":"2019","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68474-1_6"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68474-1_5"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1198\/073500101316970421"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-018-0578-6"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/509907.510013"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2012.94"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2017.8267651"},{"key":"ref27","first-page":"2487","article-title":"Hubs in space: Popular nearest neighbors in high-dimensional data","volume":"11","author":"radovanovi?","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2013.139"},{"key":"ref2","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2014","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081950"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00014"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2324796.2324813"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP.2010.5661993"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502121"},{"key":"ref23","first-page":"1817","article-title":"Enlarging hacker&#x2019;s toolbox: Deluding image recognition by attacking keypoint orientations","author":"do","year":"2012","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process (ICASSP)"},{"key":"ref26","article-title":"Invisible mask: Practical attacks on face recognition with infrared","volume":"abs 1803 4683","author":"zhou","year":"2018","journal-title":"CoRR"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ISBA.2017.7947686"},{"key":"ref50","first-page":"1","article-title":"Characterizing adversarial subspaces using local intrinsic dimensionality","author":"ma","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref51","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref10","first-page":"5019","article-title":"Adversarially robust generalization requires more data","author":"schmidt","year":"2018","journal-title":"Proc 31st Annu Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5663-3"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1002\/9780470057339.vaq002"},{"key":"ref12","article-title":"Adversarial manipulation of deep representations","author":"sabour","year":"2016","journal-title":"Proc 4th Int Conf Learn Represent (ICLR)"},{"key":"ref13","first-page":"1624","article-title":"Robustness of classifiers: From adversarial to random noise","author":"fawzi","year":"2016","journal-title":"Proc 29th Annu Conf Neural Inf Process Syst"},{"key":"ref14","article-title":"Analysis of universal adversarial perturbations","volume":"abs 1705 9554","author":"moosavi-dezfooli","year":"2017","journal-title":"CoRR"},{"key":"ref15","article-title":"Intriguing properties of adversarial examples","author":"cubuk","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref16","article-title":"A boundary tilting persepective on the phenomenon of adversarial examples","volume":"abs 1608 7690","author":"tanay","year":"2016","journal-title":"CoRR"},{"key":"ref17","article-title":"Adversarial spheres","author":"gilmer","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref18","first-page":"5133","article-title":"Analyzing the robustness of nearest neighbors to adversarial examples","volume":"80","author":"wang","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref19","article-title":"Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning","volume":"abs 1803 4765","author":"papernot","year":"2018","journal-title":"CoRR"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978392"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00175"},{"key":"ref6","first-page":"1186","article-title":"Adversarial vulnerability for any classifier","author":"fawzi","year":"2018","journal-title":"Proc 31st Annu Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.023"},{"key":"ref8","article-title":"Transferability in machine learning: From phenomena to black-box attacks using adversarial samples","volume":"abs 1605 7277","author":"papernot","year":"2016","journal-title":"CoRR"},{"key":"ref7","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)ICLR"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176343886"},{"key":"ref9","article-title":"The space of transferable adversarial examples","volume":"abs 1704 3453","author":"tram\u00e8r","year":"2017","journal-title":"CoRR"},{"key":"ref46","article-title":"Who&#x2019;s afraid of adversarial queries?: The impact of image modifications on content-based image retrieval","author":"liu","year":"2019","journal-title":"Proc ACM Int Conf Multimedia Retr (ICMR)"},{"key":"ref45","first-page":"427","article-title":"Deep neural networks are easily fooled: High confidence predictions for unrecognizable images","author":"nguyen","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00514"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5946540"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9151439\/09194069.pdf?arnumber=9194069","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:52:34Z","timestamp":1652194354000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9194069\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":51,"URL":"https:\/\/doi.org\/10.1109\/tifs.2020.3023274","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}