{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:44:07Z","timestamp":1771703047133,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200649","type":"print"},{"value":"9783031200656","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20065-6_18","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T20:24:03Z","timestamp":1667420643000},"page":"301-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Scaling Adversarial Training to\u00a0Large Perturbation Bounds"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7238-4603","authenticated-orcid":false,"given":"Sravanti","family":"Addepalli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3785-4782","authenticated-orcid":false,"given":"Samyak","family":"Jain","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7008-2417","authenticated-orcid":false,"given":"Gaurang","family":"Sriramanan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1926-1804","authenticated-orcid":false,"given":"R.","family":"Venkatesh Babu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"18_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/978-3-030-58592-1_29","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Andriushchenko","year":"2020","unstructured":"Andriushchenko, M., Croce, F., Flammarion, N., Hein, M.: Square attack: a query-efficient black-box adversarial attack via random search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 484\u2013501. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_29"},{"key":"18_CR2","unstructured":"Balaji, Y., Goldstein, T., Hoffman, J.: Instance adaptive adversarial training: improved accuracy tradeoffs in neural nets. arXiv preprint arXiv:1910.08051 (2019)"},{"key":"18_CR3","unstructured":"Carlini, N., et al.: On evaluating adversarial robustness. arXiv preprint arXiv:1902.06705 (2019)"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Gu, Q.: Rays: a ray searching method for hard-label adversarial attack. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1739\u20131747 (2020)","DOI":"10.1145\/3394486.3403225"},{"key":"18_CR5","unstructured":"Chen, T., Zhang, Z., Liu, S., Chang, S., Wang, Z.: Robust overfitting may be mitigated by properly learned smoothening. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"18_CR6","unstructured":"Croce, F., et al.: Robustbench: a standardized adversarial robustness benchmark. In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021)"},{"key":"18_CR7","unstructured":"Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00020"},{"key":"18_CR9","unstructured":"Goodfellow, I., Papernot, N.: Is attacking machine learning easier than defending it?, blog post on 15 February 2017"},{"key":"18_CR10","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"18_CR11","unstructured":"Gowal, S., Qin, C., Uesato, J., Mann, T., Kohli, P.: Uncovering the limits of adversarial training against norm-bounded adversarial examples. arXiv preprint arXiv:2010.03593 (2020)"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"18_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"18_CR14","unstructured":"Howard, J.: Imagenette dataset (2019). https:\/\/github.com\/fastai\/imagenette"},{"key":"18_CR15","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., Wilson, A.G.: Averaging weights leads to wider optima and better generalization. arXiv preprint arXiv:1803.05407 (2018)"},{"key":"18_CR16","unstructured":"Krizhevsky, A., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"18_CR17","unstructured":"Laidlaw, C., Singla, S., Feizi, S.: Perceptual adversarial robustness: defense against unseen threat models. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"18_CR18","unstructured":"Madry, A., Makelov, A., Schmidt, L., Dimitris, T., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"18_CR19","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NeurIPS Workshop on Deep Learning and Unsupervised Feature Learning (2011)"},{"key":"18_CR20","unstructured":"Pang, T., Yang, X., Dong, Y., Su, H., Zhu, J.: Bag of tricks for adversarial training. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"18_CR21","unstructured":"Rebuffi, S.A., Gowal, S., Calian, D.A., Stimberg, F., Wiles, O., Mann, T.: Fixing data augmentation to improve adversarial robustness. arXiv preprint arXiv:2103.01946 (2021)"},{"key":"18_CR22","unstructured":"Rice, L., Wong, E., Kolter, J.Z.: Overfitting in adversarially robust deep learning. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"18_CR23","unstructured":"Shaeiri, A., Nobahari, R., Rohban, M.H.: Towards deep learning models resistant to large perturbations. arXiv preprint arXiv:2003.13370 (2020)"},{"key":"18_CR24","unstructured":"Sitawarin, C., Chakraborty, S., Wagner, D.: Improving adversarial robustness through progressive hardening. arXiv preprint arXiv:2003.09347 (2020)"},{"key":"18_CR25","unstructured":"Sriramanan, G., Addepalli, S., Baburaj, A., Venkatesh Babu, R.: Guided adversarial attack for evaluating and enhancing adversarial defenses. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Stutz, D., Hein, M., Schiele, B.: Relating adversarially robust generalization to flat minima. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00771"},{"key":"18_CR27","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2013)"},{"key":"18_CR28","unstructured":"Tram\u00e8r, F., Behrmann, J., Carlini, N., Papernot, N., Jacobsen, J.H.: Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"18_CR29","unstructured":"Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., Madry, A.: Robustness may be at odds with accuracy. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"18_CR30","unstructured":"Wu, D., Xia, S.T., Wang, Y.: Adversarial weight perturbation helps robust generalization. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)","DOI":"10.5244\/C.30.87"},{"key":"18_CR33","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning (ICML) (2019)"},{"key":"18_CR34","unstructured":"Zhang, J., et al.: Attacks which do not kill training make adversarial learning stronger. In: International Conference on Machine Learning (ICML) (2020)"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20065-6_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:10:30Z","timestamp":1667779830000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20065-6_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200649","9783031200656"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20065-6_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}