{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T02:12:59Z","timestamp":1773713579235,"version":"3.50.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"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":["62202104"],"award-info":[{"award-number":["62202104"]}],"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":["62476250"],"award-info":[{"award-number":["62476250"]}],"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":["U2441239"],"award-info":[{"award-number":["U2441239"]}],"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":["U244120033"],"award-info":[{"award-number":["U244120033"]}],"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":["U24A20336"],"award-info":[{"award-number":["U24A20336"]}],"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":["62172243"],"award-info":[{"award-number":["62172243"]}],"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":["62402425"],"award-info":[{"award-number":["62402425"]}],"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":["62402418"],"award-info":[{"award-number":["62402418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M762829"],"award-info":[{"award-number":["2024M762829"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["LD24F020002"],"award-info":[{"award-number":["LD24F020002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Pioneer and Leading Goose"},{"name":"R&#x0026;D Program of Zhejiang","award":["2025C01082"],"award-info":[{"award-number":["2025C01082"]}]},{"name":"R&#x0026;D Program of Zhejiang","award":["2025C02033"],"award-info":[{"award-number":["2025C02033"]}]},{"name":"R&#x0026;D Program of Zhejiang","award":["2025C02263"],"award-info":[{"award-number":["2025C02263"]}]},{"name":"Zhejiang Provincial Priority-Funded Postdoctoral Research Project","award":["ZJ2024001"],"award-info":[{"award-number":["ZJ2024001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Dependable and Secure Comput."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tdsc.2025.3632109","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T18:44:27Z","timestamp":1762973067000},"page":"3036-3050","source":"Crossref","is-referenced-by-count":0,"title":["Mix\n                    <sup>2<\/sup>\n                    Aug: Revisiting Mixing-Based Augmentations for Improving Robust Generalization of Adversarial Training"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7017-5513","authenticated-orcid":false,"given":"Zhaozhe","family":"Hu","sequence":"first","affiliation":[{"name":"Fujian Province Key Laboratory of Information Security and Network Systems, and College of Computer Science and Big Data, Fuzhou University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-6105","authenticated-orcid":false,"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"Fujian Province Key Laboratory of Information Security and Network Systems, and College of Computer Science and Big Data, Fuzhou University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8087-9769","authenticated-orcid":false,"given":"Jia-Li","family":"Yin","sequence":"additional","affiliation":[{"name":"Fujian Province Key Laboratory of Information Security and Network Systems, and College of Computer Science and Big Data, Fuzhou University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7999-3396","authenticated-orcid":false,"given":"Bo-Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4056-9755","authenticated-orcid":false,"given":"Yaguan","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4268-372X","authenticated-orcid":false,"given":"Shouling","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00414"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3186918"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2025.3548970"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3521942"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2022.3179131"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3430508"},{"key":"ref7","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2017"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3263637"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3518698"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2025.3548402"},{"key":"ref11","first-page":"5019","article-title":"Adversarially robust generalization requires more data","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Schmidt"},{"key":"ref12","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rice"},{"key":"ref13","first-page":"7229","article-title":"Robust overfitting may be mitigated by properly learned smoothening","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref14","first-page":"29935","article-title":"Data augmentation can improve robustness","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Rebuffi"},{"key":"ref15","article-title":"Data augmentation alone can improve adversarial training","author":"Li","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref19","first-page":"1188","article-title":"Vapnik-Chervonenkis dimension of neural nets","volume-title":"The Handbook of Brain Theory and Neural Networks","volume":"2","author":"Bartlett","year":"2003"},{"key":"ref20","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref22","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":"ref23","first-page":"2196","article-title":"Minimally distorted adversarial examples with a fast adaptive boundary attack","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02362"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2025.3529197"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2023.3261327"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.129124"},{"key":"ref29","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref30","first-page":"8909","article-title":"Improving adversarial robustness requires revisiting misclassified examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref31","first-page":"25595","article-title":"Understanding robust overfitting of adversarial training and beyond","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref32","article-title":"Self-ensemble adversarial training for improved robustness","author":"Wang","year":"2022"},{"key":"ref33","first-page":"17258","article-title":"Robustness and accuracy could be reconcilable by (proper) definition","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pang"},{"key":"ref34","article-title":"The effectiveness of random forgetting for robust generalization","author":"Ramkumar","year":"2024"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2023.3269012"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3127439"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3411302"},{"key":"ref38","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017"},{"key":"ref39","article-title":"Averaging weights leads to wider optima and better generalization","author":"Izmailov","year":"2018"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3165889"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00129"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref44","first-page":"6241","article-title":"Spectrally-normalized margin bounds for neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Bartlett"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20212-4"},{"key":"ref46","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref50","first-page":"2958","article-title":"Adversarial weight perturbation helps robust generalization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20817"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00081"},{"key":"ref54","article-title":"AugMix: A simple data processing method to improve robustness and uncertainty","author":"Hendrycks","year":"2019"},{"key":"ref55","article-title":"FMix: Enhancing mixed sample data augmentation","author":"Harris","year":"2020"},{"key":"ref56","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019"},{"key":"ref57","first-page":"17901","article-title":"Defending against image corruptions through adversarial augmentations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Calian"},{"key":"ref58","article-title":"An image is worth 16 x 16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"ref59","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford"},{"key":"ref60","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Ren"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"}],"container-title":["IEEE Transactions on Dependable and Secure Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8858\/11434575\/11242025.pdf?arnumber=11242025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T01:15:31Z","timestamp":1773710131000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11242025\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":61,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tdsc.2025.3632109","relation":{},"ISSN":["1545-5971","1941-0018","2160-9209"],"issn-type":[{"value":"1545-5971","type":"print"},{"value":"1941-0018","type":"electronic"},{"value":"2160-9209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}