{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:45:34Z","timestamp":1783953934769,"version":"3.55.0"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"U.S. Department of Energy (DOE), Office of Science (SC), Advanced Scientific Computing Research Program","award":["DE-SC-0012704"],"award-info":[{"award-number":["DE-SC-0012704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tcyb.2024.3380437","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T18:34:43Z","timestamp":1712687683000},"page":"5141-5151","source":"Crossref","is-referenced-by-count":5,"title":["Exploring Robust Features for Improving Adversarial Robustness"],"prefix":"10.1109","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5359-6296","authenticated-orcid":false,"given":"Hong","family":"Wang","sequence":"first","affiliation":[{"name":"Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuefan","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4378-6448","authenticated-orcid":false,"given":"Shinjae","family":"Yoo","sequence":"additional","affiliation":[{"name":"Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1429-4543","authenticated-orcid":false,"given":"Yuewei","family":"Lin","sequence":"additional","affiliation":[{"name":"Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Intriguing properties of neural networks","volume-title":"Proc. ICLR","author":"Szegedy"},{"key":"ref2","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. ICLR","author":"Goodfellow"},{"key":"ref3","first-page":"1","article-title":"Adversarially robust generalization requires more data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Schmidt"},{"key":"ref4","first-page":"1","article-title":"Adversarial examples are not bugs, they are features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ilyas"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16424"},{"key":"ref8","first-page":"1","article-title":"Adversarial feature desensitization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Bashivan"},{"key":"ref9","first-page":"1","article-title":"Class-disentanglement and applications in adversarial detection and defense","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Yang"},{"key":"ref10","first-page":"12835","article-title":"Towards defending against adversarial examples via attack-invariant features","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref11","first-page":"1","article-title":"Distilling robust and non-robust features in adversarial examples by information bottleneck","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kim"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/628"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_18"},{"key":"ref14","first-page":"5102","article-title":"Domain agnostic learning with disentangled representations","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Peng"},{"key":"ref15","article-title":"Domain generalization: A survey","author":"Zhou","year":"2021","journal-title":"arXiv:2103.02503"},{"key":"ref16","first-page":"322","article-title":"DIVA: Domain invariant variational autoencoders","volume-title":"Proc. 3rd Conf. Med. Imag. Deep Learn.","author":"Ilse"},{"key":"ref17","first-page":"1","article-title":"Adversarial examples in the physical world","volume-title":"Proc. ICLR Workshop","author":"Kurakin"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref21","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref22","first-page":"2196","article-title":"Minimally distorted adversarial examples with a fast adaptive boundary attack","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.106"},{"key":"ref25","first-page":"1","article-title":"Measuring invariances in deep networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00467"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00175"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00080"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3041481"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2882908"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3209175"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3125345"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3085744"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3071395"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00816"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20571"},{"key":"ref37","first-page":"1","article-title":"Reliable adversarial distillation with unreliable teachers","volume-title":"Proc. ICLR","author":"Zhu"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01304"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01484"},{"key":"ref40","first-page":"1","article-title":"Squeeze training for adversarial robustness","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref41","article-title":"Adversarial logit pairing","author":"Kannan","year":"2018","journal-title":"arXiv:1803.06373"},{"key":"ref42","first-page":"1","article-title":"Metric learning for adversarial robustness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mao"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00665"},{"key":"ref44","first-page":"1","article-title":"Adversarial training for free!","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst.","author":"Shafahi"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5816"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_40"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00405"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00756"},{"key":"ref49","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref50","first-page":"1","article-title":"Defense against adversarial attacks using feature scattering-based adversarial training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref51","first-page":"1","article-title":"Revisiting Hilbert-Schmidt information bottleneck for adversarial robustness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wang"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00059"},{"key":"ref53","first-page":"501","article-title":"Improving adversarial robustness requires revisiting misclassified examples","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Wang"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3146388"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2914099"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"ref57","article-title":"Wasserstein auto-encoders","author":"Tolstikhin","year":"2017","journal-title":"arXiv:1711.01558"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01500"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3110128"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00671"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01284-z"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3000480"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01543"},{"key":"ref65","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Rice"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298188"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref68","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Athalye"},{"key":"ref69","first-page":"1","article-title":"Adversarial risk and the dangers of evaluating against weak attacks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Uesato"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6221036\/10646531\/10495132-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/10646531\/10495132.pdf?arnumber=10495132","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T04:03:12Z","timestamp":1725163392000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10495132\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":69,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2024.3380437","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"value":"2168-2267","type":"print"},{"value":"2168-2275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}