{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:36:16Z","timestamp":1757619376590,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819698653"},{"type":"electronic","value":"9789819698660"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-9866-0_10","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T09:25:28Z","timestamp":1753262728000},"page":"110-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boosting Adversarial Robustness Through Structure-Guided Adversarial Distillation"],"prefix":"10.1007","author":[{"given":"Xiaolong","family":"Li","sequence":"first","affiliation":[]},{"given":"Yanming","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Chengsi","family":"Du","sequence":"additional","affiliation":[]},{"given":"Dengjin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Mingrui","family":"Lao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"issue":"9","key":"10_CR1","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s42256-022-00520-5","volume":"4","author":"Y Almalioglu","year":"2022","unstructured":"Almalioglu, Y., Turan, M., Trigoni, N., et al.: Deep learning-based robust positioning for all-weather autonomous driving. Nat. Mach. Intell. 4(9), 749\u2013760 (2022)","journal-title":"Nat. Mach. Intell."},{"key":"10_CR2","unstructured":"Baevski, A., Zhou, Y., Mohamed, A., et al.: Wav2vec 2.0: a framework for self-supervised learning of speech representations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 12449\u201312460 (2020)"},{"key":"10_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332","volume":"110","author":"X Ma","year":"2021","unstructured":"Ma, X., Niu, Y., Gu, L., et al.: Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recogn. 110, 107332 (2021)","journal-title":"Pattern Recogn."},{"key":"10_CR4","unstructured":"Touvron, H., Martin, L., Stone, K., et al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"10_CR5","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"10_CR6","unstructured":"Madry, A., Makelov, A., Schmidt, L., et al.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (2018)"},{"key":"10_CR7","unstructured":"Rice, L., Wong, E., Kolter, Z.: Overfitting in adversarially robust deep learning. In: International Conference on Machine Learning, pp. 8093\u20138104 (2020)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Goldblum, M., Fowl, L., Feizi, S., et al.: Adversarially robust distillation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 4, pp. 3996\u20134003 (2020)","DOI":"10.1609\/aaai.v34i04.5816"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Zi, B., Zhao, S., Ma, X., et al.: Revisiting adversarial robustness distillation: robust soft labels make student better. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16443\u201316452 (2021)","DOI":"10.1109\/ICCV48922.2021.01613"},{"key":"10_CR10","unstructured":"Zhu, J., Yao, J., Han, B., et al.: Reliable adversarial distillation with unreliable teachers. In: International Conference on Learning Representations (2021)"},{"key":"10_CR11","unstructured":"Maroto, J., Ortiz-Jim\u00e9nez, G., Frossard, P.: On the benefits of knowledge distillation for adversarial robustness. arXiv preprint arXiv:2203.07159 (2022)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Zhao, S., Yu, J., Sun, Z., et al.: Enhanced accuracy and robustness via multi-teacher adversarial distillation. In: European Conference on Computer Vision, pp. 585\u2013602 (2022)","DOI":"10.1007\/978-3-031-19772-7_34"},{"key":"10_CR13","unstructured":"Liu, X., Kuang, H., Lin, X., et al.: CAT: collaborative adversarial training. arXiv preprint arXiv:2303.14922 (2023)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Lao, M., Guo, Y., Liu, Y., et al.: From superficial to deep: language bias driven curriculum learning for visual question answering. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3370\u20133379 (2021)","DOI":"10.1145\/3474085.3475492"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Wu, G., Gong, S.: Peer collaborative learning for online knowledge distillation. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i12.17234"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Huang, B., Chen, M., Wang, Y., et al.: Boosting accuracy and robustness of student models via adaptive adversarial distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24668\u201324677 (2023)","DOI":"10.1109\/CVPR52729.2023.02363"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR18","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":"10_CR19","unstructured":"Zhang, H., Yu, Y., Jiao, J., et al.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning, pp. 7472\u20137482 (2019)"},{"key":"10_CR20","unstructured":"Gowal, S., Qin, C., Uesato, J., et al.: Uncovering the limits of adversarial training against norm-bounded adversarial examples. arXiv preprint arXiv:2010.03593 (2020)"},{"key":"10_CR21","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"},{"key":"10_CR22","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":"10_CR23","unstructured":"Croce, F., Hein, M.: Minimally distorted adversarial examples with a fast adaptive boundary attack. In: Proceedings of the International Conference on Machine Learning (2020)"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Andriushchenko, M., Croce, F., Flammarion, N., et al.: Square attack: a query-efficient black-box adversarial attack via random search. In: Proceedings of the European Conference on Computer Vision, pp. 2206\u20132216 (2020)","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"10_CR25","unstructured":"Pang, T., Yang, X., Dong, Y., et al.: Bag of tricks for adversarial training. In: International Conference on Learning Representations (2021)"},{"key":"10_CR26","unstructured":"Zhang, J., Zhu, J., Niu, G., et al.: Geometry-aware instance-reweighted adversarial training. In: International Conference on Learning Representations (2021)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Yin, S., Xiao, Z., Song, M., et al.: Adversarial distillation based on slack matching and attribution region alignment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24605\u201324614 (2024)","DOI":"10.1109\/CVPR52733.2024.02323"},{"key":"10_CR28","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Lao, M., Pu, N., Liu, Y., et al.: Multi-domain lifelong visual question answering via self-critical distillation. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 4747\u20134758 (2023)","DOI":"10.1145\/3581783.3612121"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9866-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T19:38:21Z","timestamp":1757273901000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9866-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698653","9789819698660"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9866-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}