{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:35:33Z","timestamp":1787013333090,"version":"build-2736575974"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tpami.2023.3286772","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T14:00:03Z","timestamp":1687183203000},"page":"1-14","source":"Crossref","is-referenced-by-count":13,"title":["Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3748-4160","authenticated-orcid":false,"given":"Chun Pong","family":"Lau","sequence":"first","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7568-2454","authenticated-orcid":false,"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Souri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-An","family":"Lin","sequence":"additional","affiliation":[{"name":"Adobe, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0944-8242","authenticated-orcid":false,"given":"Soheil","family":"Feizi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Maryland, College Park, College Park, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rama","family":"Chellappa","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering and the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Resisting adversarial attacks by -winners-take-all","author":"xiao","year":"2019"},{"key":"ref57","article-title":"Testing robustness against unforeseen adversaries","author":"kang","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1907377117"},{"key":"ref56","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","author":"croce","year":"2020","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3184262"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00090"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01455"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref53","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref52","article-title":"Fast is better than free: Revisiting adversarial training","author":"wong","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref11","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2016"},{"key":"ref55","article-title":"Improving adversarial robustness requires revisiting misclassified examples","author":"wang","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref10","first-page":"284","article-title":"Synthesizing robust adversarial examples","author":"athalye","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref54","article-title":"Improved regularization of convolutional neural networks with cutout","author":"devries","year":"2017"},{"key":"ref17","article-title":"Mixup inference: Better exploiting mixup to defend adversarial attacks","author":"pang","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","first-page":"5498","article-title":"The odds are odd: A statistical test for detecting adversarial examples","author":"roth","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","first-page":"5286","article-title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","author":"wong","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","article-title":"Defense-GAN: Protecting classifiers against adversarial attacks using generative models","author":"samangouei","year":"2018"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref50","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"hendrycks","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref46","article-title":"NICE: Non-linear independent components estimation","author":"dinh","year":"2015","journal-title":"Proc 3rd Int Conf Learn Representations"},{"key":"ref45","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","author":"rice","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref48","first-page":"10236","article-title":"Glow: Generative flow with invertible 1x1 convolutions","author":"kingma","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst 31 Annu Conf Neural Inf Process Syst"},{"key":"ref47","article-title":"Density estimation using real NVP","author":"dinh","year":"2017","journal-title":"Proc 5th Int Conf Learn Representations"},{"key":"ref42","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","author":"verma","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref41","article-title":"Mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00035"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357369"},{"key":"ref49","first-page":"6640","article-title":"Adversarial robustness against the union of multiple perturbation models","author":"maini","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","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"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref9","first-page":"1186","article-title":"Adversarial vulnerability for any classifier","author":"fawzi","year":"2018","journal-title":"Proc 32nd Int Conf Neural Inform Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref3","first-page":"3938","article-title":"Wasserstein smoothing: Certified robustness against Wasserstein adversarial attacks","author":"levine","year":"2020","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref6","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/bs.host.2023.01.001"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301541"},{"key":"ref35","article-title":"Dual manifold adversarial robustness: Defense against Lp and non-Lp adversarial attacks","author":"lin","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref34","article-title":"Attribute-guided encryption with facial texture masking","author":"lau","year":"2023"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00655"},{"key":"ref36","article-title":"The robust manifold defense: Adversarial training using generative models","author":"jalal","year":"2017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00714"},{"key":"ref30","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref33","article-title":"DiffProtect: Generate adversarial examples with diffusion models for facial privacy protection","author":"liu","year":"2023"},{"key":"ref32","article-title":"Understanding and mitigating the tradeoff between robustness and accuracy","author":"raghunathan","year":"2020"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref39","article-title":"Towards the first adversarially robust neural network model on MNIST","author":"schott","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref38","first-page":"1736","article-title":"On breaking deep generative model-based defenses and beyond","author":"chen","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","first-page":"9155","article-title":"Confidence-calibrated adversarial training: Generalizing to unseen attacks","author":"stutz","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref23","article-title":"Certified defenses for adversarial patches","author":"chiang","year":"2020"},{"key":"ref26","article-title":"Perceptual adversarial robustness: Defense against unseen threat models","author":"laidlaw","year":"2021","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref25","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017"},{"key":"ref20","first-page":"10877","article-title":"Semidefinite relaxations for certifying robustness to adversarial examples","author":"raghunathan","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref64","article-title":"Robustness may be at odds with accuracy","author":"tsipras","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5888"},{"key":"ref66","first-page":"573","article-title":"Invertible residual networks","author":"behrmann","year":"2019","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref21","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","author":"cohen","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref65","first-page":"211","article-title":"Random smoothing might be unable to certify $l_\\infty$l? robustness for high-dimensional images","volume":"21","author":"blum","year":"2020","journal-title":"J Mach Learn Res"},{"key":"ref28","article-title":"Constructing unrestricted adversarial examples with generative models","author":"song","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","article-title":"Functional adversarial attacks","author":"laidlaw","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","article-title":"Fine-grained synthesis of unrestricted adversarial examples","author":"poursaeed","year":"2021"},{"key":"ref60","article-title":"On the effectiveness of adversarial training against common corruptions","author":"kireev","year":"2021"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref61","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"2018"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/10155464.pdf?arnumber=10155464","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T11:02:27Z","timestamp":1696417347000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10155464\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":66,"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3286772","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}