{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:31:43Z","timestamp":1743093103796,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732287"},{"type":"electronic","value":"9783031732294"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73229-4_3","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:09Z","timestamp":1729782189000},"page":"34-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rethinking Fast Adversarial Training: A Splitting Technique to\u00a0Overcome Catastrophic Overfitting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3991-0584","authenticated-orcid":false,"given":"Masoumeh","family":"Zareapoor","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0263-1661","authenticated-orcid":false,"given":"Pourya","family":"Shamsolmoali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"3_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-3-031-20065-6_18","volume-title":"Computer Vision \u2013 ECCV 2022","author":"S Addepalli","year":"2022","unstructured":"Addepalli, S., Jain, S., Sriramanan, G., Venkatesh Babu, R.: Scaling adversarial training to large perturbation bounds. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13665, pp. 301\u2013316. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20065-6_18"},{"key":"3_CR2","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":"3_CR3","first-page":"16048","volume":"33","author":"M Andriushchenko","year":"2020","unstructured":"Andriushchenko, M., Flammarion, N.: Understanding and improving fast adversarial training. Adv. Neural. Inf. Process. Syst. 33, 16048\u201316059 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10107-022-01901-9","volume":"201","author":"D Applegate","year":"2023","unstructured":"Applegate, D., Hinder, O., Lu, H., Lubin, M.: Faster first-order primal-dual methods for linear programming using restarts and sharpness. Math. Program. 201(1), 133\u2013184 (2023)","journal-title":"Math. Program."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357 (2017)","DOI":"10.1109\/SP.2017.49"},{"issue":"4","key":"3_CR6","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1137\/22M1481865","volume":"4","author":"A Chambolle","year":"2022","unstructured":"Chambolle, A., Contreras, J.P.: Accelerated Bregman primal-dual methods applied to optimal transport and Wasserstein barycenter problems. SIAM J. Math. Data Sci. 4(4), 1369\u20131395 (2022)","journal-title":"SIAM J. Math. Data Sci."},{"key":"3_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, pp. 2206\u20132216 (2020)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"3_CR10","unstructured":"Golgooni, Z., Saberi, M., Eskandar, M., Rohban, M.H.: ZeroGrad: mitigating and explaining catastrophic overfitting in FGSM adversarial training. arXiv preprint arXiv:2103.15476 (2021)"},{"key":"3_CR11","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2014)"},{"key":"3_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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_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, Part IV. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"He, Z., Li, T., Chen, S., Huang, X.: Investigating catastrophic overfitting in fast adversarial training: a self-fitting perspective. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2313\u20132320 (2023)","DOI":"10.1109\/CVPRW59228.2023.00227"},{"key":"3_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/978-3-031-19772-7_33","volume-title":"Computer Vision \u2013 ECCV 2022","author":"X Jia","year":"2022","unstructured":"Jia, X., et al.: Prior-guided adversarial initialization for fast adversarial training. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13664, pp. 567\u2013584. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19772-7_33"},{"key":"3_CR16","first-page":"12881","volume":"35","author":"P de Jorge Aranda","year":"2022","unstructured":"de Jorge Aranda, P., et al.: Make some noise: reliable and efficient single-step adversarial training. Adv. Neural. Inf. Process. Syst. 35, 12881\u201312893 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR17","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Li, T., Wu, Y., Chen, S., Fang, K., Huang, X.: Subspace adversarial training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13409\u201313418 (2022)","DOI":"10.1109\/CVPR52688.2022.01305"},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1007\/s10957-017-1061-z","volume":"172","author":"J Liang","year":"2017","unstructured":"Liang, J., Fadili, J., Peyr\u00e9, G.: Local convergence properties of Douglas-Rachford and alternating direction method of multipliers. J. Optim. Theory Appl. 172, 874\u2013913 (2017)","journal-title":"J. Optim. Theory Appl."},{"key":"3_CR20","unstructured":"Lindb\u00e4ck, J., Wang, Z., Johansson, M.: Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs. Adv. Neural Inf. Process. Syst. 36 (2023)"},{"key":"3_CR21","unstructured":"Liu, X., Chakraborty, S., Sun, Y., Huang, F.: Rethinking adversarial policies: a generalized attack formulation and provable defense in RL. In: International Conference on Learning Representations (2024)"},{"key":"3_CR22","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. International Conference on Learning Representations (2018)"},{"key":"3_CR23","unstructured":"Mai, V.V., Lindb\u00e4ck, J., Johansson, M.: A fast and accurate splitting method for optimal transport: analysis and implementation. In: International Conference on Learning Representations (2022)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Uesato, J., Frossard, P.: Robustness via curvature regularization, and vice versa. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9078\u20139086 (2019)","DOI":"10.1109\/CVPR.2019.00929"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Park, G.Y., Lee, S.W.: Reliably fast adversarial training via latent adversarial perturbation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7758\u20137767 (2021)","DOI":"10.1109\/ICCV48922.2021.00766"},{"key":"3_CR26","unstructured":"Poon, C., Liang, J.: Trajectory of alternating direction method of multipliers and adaptive acceleration. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"3_CR27","unstructured":"Qin, C., et al.: Adversarial robustness through local linearization. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"3_CR28","unstructured":"Rocamora, E.A., Liu, F., Chrysos, G.G., Olmos, P.M., Cevher, V.: Efficient local linearity regularization to overcome catastrophic overfitting. In: International Conference on Learning Representations (2024)"},{"key":"3_CR29","unstructured":"Shaeiri, A., Nobahari, R., Rohban, M.H.: Towards deep learning models resistant to large perturbations. arXiv preprint arXiv:2003.13370 (2020)"},{"key":"3_CR30","unstructured":"Shafahi, A., et al.: Adversarial training for free! Adv. Neural Inf. Process. Syste. 32 (2019)"},{"key":"3_CR31","unstructured":"Song, C., He, K., Wang, L., Hopcroft, J.E.: Improving the generalization of adversarial training with domain adaptation. In: International Conference on Learning Representations (2019)"},{"key":"3_CR32","first-page":"20297","volume":"33","author":"G Sriramanan","year":"2020","unstructured":"Sriramanan, G., Addepalli, S., Baburaj, A., et al.: Guided adversarial attack for evaluating and enhancing adversarial defenses. Adv. Neural. Inf. Process. Syst. 33, 20297\u201320308 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR33","first-page":"11821","volume":"34","author":"G Sriramanan","year":"2021","unstructured":"Sriramanan, G., Addepalli, S., Baburaj, A., et al.: Towards efficient and effective adversarial training. Adv. Neural. Inf. Process. Syst. 34, 11821\u201311833 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"3_CR34","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1137\/18M1163993","volume":"30","author":"A Themelis","year":"2020","unstructured":"Themelis, A., Patrinos, P.: Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence results. SIAM J. Optim. 30(1), 149\u2013181 (2020)","journal-title":"SIAM J. Optim."},{"key":"3_CR35","doi-asserted-by":"crossref","unstructured":"Tsiligkaridis, T., Roberts, J.: Understanding and increasing efficiency of Frank-Wolfe adversarial training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 50\u201359 (2022)","DOI":"10.1109\/CVPR52688.2022.00015"},{"key":"3_CR36","unstructured":"Wong, E., Rice, L., Kolter, J.Z.: Fast is better than free: revisiting adversarial training. In: International Conference on Learning Representations (2020)"},{"key":"3_CR37","unstructured":"Wu, D., Xia, S.T., Wang, Y.: Adversarial weight perturbation helps robust generalization. Adv. Neural Inf. Process. Syst. (2020)"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Xie, Y., Li, Z., Shi, C., Liu, J., Chen, Y., Yuan, B.: Enabling fast and universal audio adversarial attack using generative model. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 14129\u201314137 (2021)","DOI":"10.1609\/aaai.v35i16.17663"},{"key":"3_CR39","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":"3_CR40","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E., El\u00a0Ghaoui, L., Jordan, M.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning, pp. 7472\u20137482 (2019)"},{"key":"3_CR41","unstructured":"Zhang, Y., Zhang, G., Khanduri, P., Hong, M., Chang, S., Liu, S.: Revisiting and advancing fast adversarial training through the lens of bi-level optimization. In: International Conference on Machine Learning, pp. 26693\u201326712 (2022)"},{"key":"3_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, M., Zhang, L., Kong, Y., Yin, B.: Fast adversarial training with smooth convergence. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4720\u20134729 (2023)","DOI":"10.1109\/ICCV51070.2023.00435"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73229-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T15:03:24Z","timestamp":1729782204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73229-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031732287","9783031732294"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73229-4_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}