{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:39:12Z","timestamp":1775745552126,"version":"3.50.1"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171248"],"award-info":[{"award-number":["62171248"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220818101012025"],"award-info":[{"award-number":["JCYJ20220818101012025"]}]},{"name":"PCNL KEY Project","award":["PCL2021A07"],"award-info":[{"award-number":["PCL2021A07"]}]},{"name":"Research Center for Computer Network (Shenzhen) Ministry of Education"},{"name":"JST Strategic Basic Research Programs, ACT-X","award":["JPMJAX21AF"],"award-info":[{"award-number":["JPMJAX21AF"]}]},{"name":"JSPS Grants-in-Aid for Scientific Research (KAKENHI), Early-Career Scientists, Japan","award":["22K17955"],"award-info":[{"award-number":["22K17955"]}]},{"name":"Institute for AI and Beyond, UTokyo"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1109\/tnnls.2023.3281872","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T17:23:34Z","timestamp":1686763414000},"page":"14878-14888","source":"Crossref","is-referenced-by-count":15,"title":["On the Effectiveness of Adversarial Training Against Backdoor Attacks"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7158-2613","authenticated-orcid":false,"given":"Yinghua","family":"Gao","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxian","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Complexity Science and Engineering, The University of Tokyo, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanhao","family":"Gan","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-982X","authenticated-orcid":false,"given":"Shu-Tao","family":"Xia","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7353-5079","authenticated-orcid":false,"given":"Gang","family":"Niu","sequence":"additional","affiliation":[{"name":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6658-6743","authenticated-orcid":false,"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"Department of Complexity Science and Engineering, The University of Tokyo, Chiba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.06.008"},{"key":"ref2","first-page":"1","article-title":"Improving adversarial robustness via channel-wise activation suppressing","volume-title":"Proc. ICLR","author":"Bai"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8802997"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414862"},{"key":"ref5","article-title":"DP-InstaHide: Provably defusing poisoning and backdoor attacks with differentially private data augmentations","author":"Borgnia","year":"2021","journal-title":"arXiv:2103.02079"},{"key":"ref6","article-title":"Poisoning and backdooring contrastive learning","author":"Carlini","year":"2021","journal-title":"arXiv:2106.09667"},{"key":"ref7","article-title":"Targeted backdoor attacks on deep learning systems using data poisoning","author":"Chen","year":"2017","journal-title":"arXiv:1712.05526"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-37228-6_15"},{"key":"ref9","article-title":"What doesn\u2019t kill you makes you Robust(ER): How to adversarially train against data poisoning","author":"Geiping","year":"2021","journal-title":"arXiv:2102.13624"},{"key":"ref10","article-title":"Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses","author":"Goldblum","year":"2020","journal-title":"arXiv:2012.10544"},{"key":"ref11","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. ICLR","author":"Goodfellow"},{"key":"ref12","article-title":"BadNets: Identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017","journal-title":"arXiv:1708.06733"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","article-title":"On the effectiveness of mitigating data poisoning attacks with gradient shaping","author":"Hong","year":"2020","journal-title":"arXiv:2002.11497"},{"key":"ref15","first-page":"14900","article-title":"Anti-backdoor learning: Training clean models on poisoned data","volume-title":"Proc. NeurIPS","author":"Li"},{"key":"ref16","first-page":"1","article-title":"Neural attention distillation: Erasing backdoor triggers from deep neural networks","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref17","first-page":"1","article-title":"Untargeted backdoor watermark: Towards harmless and stealthy dataset copyright protection","volume-title":"Proc. NeurIPS","author":"Li"},{"key":"ref18","article-title":"Backdoor learning: A survey","author":"Li","year":"2020","journal-title":"arXiv:2007.08745"},{"key":"ref19","first-page":"1","article-title":"Few-shot backdoor attacks on visual object tracking","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23291"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref24","first-page":"6640","article-title":"Adversarial robustness against the union of multiple perturbation models","volume-title":"Proc. ICML","author":"Maini"},{"key":"ref25","first-page":"1","article-title":"Wanet\u2013imperceptible warping-based backdoor attack","volume-title":"Proc. ICLR","author":"Nguyen"},{"key":"ref26","first-page":"1","article-title":"Bag of tricks for adversarial training","volume-title":"Proc. ICLR","author":"Pang"},{"key":"ref27","first-page":"16209","article-title":"Better safe than sorry: Preventing delusive adversaries with adversarial training","volume-title":"Proc. NeurIPS","author":"Tao"},{"key":"ref28","first-page":"1","article-title":"Adversarial training and robustness for multiple perturbations","volume-title":"Proc. NeurIPS","author":"Tramer"},{"key":"ref29","article-title":"Label-consistent backdoor attacks","author":"Turner","year":"2019","journal-title":"arXiv:1912.02771"},{"issue":"11","key":"ref30","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref31","first-page":"1","article-title":"Improving adversarial robustness requires revisiting misclassified examples","volume-title":"Proc. ICLR","author":"Wang"},{"key":"ref32","article-title":"RAB: Provable robustness against backdoor attacks","author":"Weber","year":"2020","journal-title":"arXiv:2003.08904"},{"key":"ref33","first-page":"11973","article-title":"On the trade-off between adversarial and backdoor robustness","volume-title":"Proc. NeurIPS","author":"Weng"},{"key":"ref34","first-page":"2958","article-title":"Adversarial weight perturbation helps robust generalization","volume-title":"Proc. NeurIPS","author":"Wu"},{"key":"ref35","first-page":"1","article-title":"Spatially transformed adversarial examples","volume-title":"Proc. ICLR","author":"Xiao"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref37","article-title":"Adversarial unlearning of backdoors via implicit hypergradient","author":"Zeng","year":"2021","journal-title":"arXiv:2110.03735"},{"key":"ref38","first-page":"1","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. ICML","author":"Zhang"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1710.09412"},{"key":"ref40","first-page":"11278","article-title":"Attacks which do not kill training make adversarial learning stronger","volume-title":"Proc. ICML","author":"Zhang"},{"key":"ref41","first-page":"1","article-title":"Geometry-aware instance-reweighted adversarial training","volume-title":"Proc. ICLR","author":"Zhang"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10707065\/10153093.pdf?arnumber=10153093","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T17:46:17Z","timestamp":1728409577000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10153093\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":41,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2023.3281872","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}