{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T13:00:16Z","timestamp":1793278816708,"version":"4.5.0"},"reference-count":47,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["U22B2017"],"award-info":[{"award-number":["U22B2017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076105"],"award-info":[{"award-number":["62076105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"International Cooperation Foundation of Hubei Province, China","doi-asserted-by":"publisher","award":["2024EHA032"],"award-info":[{"award-number":["2024EHA032"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3533925","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T18:28:39Z","timestamp":1737743319000},"page":"1613-1623","source":"Crossref","is-referenced-by-count":13,"title":["Parameter Interpolation Adversarial Training for Robust Image Classification"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7345-2405","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6725-8319","authenticated-orcid":false,"given":"Yichen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7627-4604","authenticated-orcid":false,"given":"Kun","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8681-6075","authenticated-orcid":false,"given":"John E.","family":"Hopcroft","sequence":"additional","affiliation":[{"name":"Computer Science Department, Cornell University, Ithaca, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","first-page":"1106","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. 26th Annu. Conf. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2015-350"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-513"},{"key":"ref8","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Goodfellow"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00774"},{"key":"ref10","article-title":"Adversarial logit pairing","author":"Kannan","year":"2018","journal-title":"arXiv:1803.06373"},{"key":"ref11","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. 36th Int. Conf. Mach. Learn. (ICML)","author":"Zhang"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3303934"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3310352"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3184262"},{"key":"ref16","first-page":"23258","article-title":"Probabilistic margins for instance reweighting in adversarial training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3236180"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00035"},{"key":"ref19","first-page":"11278","article-title":"Attacks which do not kill training make adversarial learning stronger","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3381180"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3297791"},{"key":"ref22","first-page":"1","article-title":"Exploring memorization in adversarial training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dong"},{"key":"ref23","first-page":"1","article-title":"Improving adversarial robustness requires revisiting misclassified examples","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref25","first-page":"99","article-title":"Adversarial examples in the physical world","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kurakin"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref28","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref29","first-page":"1","article-title":"MMA training: Direct input space margin maximization through adversarial training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ding"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01578"},{"key":"ref31","first-page":"1","article-title":"Geometry-aware instance-reweighted adversarial training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01543"},{"key":"ref33","first-page":"1","article-title":"Reducing excessive margin to achieve a better accuracy vs. robustness trade-off","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Rade"},{"key":"ref34","first-page":"25595","article-title":"Understanding robust overfitting of adversarial training and beyond","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Yu"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02364"},{"key":"ref36","first-page":"1","article-title":"Squeeze training for adversarial robustness","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref37","first-page":"1","article-title":"When adversarial training meets vision transformers: Recipes from training to architecture","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mo"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00792"},{"key":"ref39","first-page":"1","article-title":"Robust overfitting may be mitigated by properly learned smoothening","volume-title":"Proc. ICLR","author":"Chen"},{"key":"ref40","first-page":"876","article-title":"Averaging weights leads to wider optima and better generalization","volume-title":"Proc. UAI","author":"Izmailov"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01969"},{"key":"ref42","first-page":"1","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref45","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref46","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Touvron"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/ac9830"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/10852411.pdf?arnumber=10852411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T20:09:10Z","timestamp":1740082150000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10852411\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3533925","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}