{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T01:16:15Z","timestamp":1781918175970,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811989902","type":"print"},{"value":"9789811989919","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8991-9_30","type":"book-chapter","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T08:04:02Z","timestamp":1674029042000},"page":"424-438","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Defending Adversarial Examples by\u00a0Negative Correlation Ensemble"],"prefix":"10.1007","author":[{"given":"Wenjian","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linghao","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"30_CR1","unstructured":"Abbasi, M., Gagn\u00e9, C.: Robustness to adversarial examples through an ensemble of specialists. In: Proceedings of the 5th International Conference on Learning Representations (2017)"},{"key":"30_CR2","unstructured":"Bagnall, A., Bunescu, R., Stewart, G.: Training ensembles to detect adversarial examples. arXiv preprint arXiv:1712.04006 (2017)"},{"key":"30_CR3","unstructured":"Brendel, W., Rauber, J., Bethge, M.: Decision-based adversarial attacks: Reliable attacks against black-box machine learning models. In: Proceedings of the 6th International Conference on Learning Representations (2018)"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: Proceedings of the 2017 IEEE Symposium on Security and Privacy, pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"issue":"3","key":"30_CR5","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11063-005-1084-6","volume":"21","author":"ZSH Chan","year":"2005","unstructured":"Chan, Z.S.H., Kasabov, N.K.: A preliminary study on negative correlation learning via correlation-corrected data (NCCD). Neural Process. Lett. 21(3), 207\u2013214 (2005)","journal-title":"Neural Process. Lett."},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Chen, P., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.: ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 15\u201326. ACM (2017)","DOI":"10.1145\/3128572.3140448"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Dabouei, A., Soleymani, S., Taherkhani, F., Dawson, J.M., Nasrabadi, N.M.: Exploiting joint robustness to adversarial perturbations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1119\u20131128. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00120"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185\u20139193. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"30_CR9","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Proceedings of the 3rd International Conference on Learning Representations (2015)"},{"key":"30_CR10","unstructured":"Kariyappa, S., Qureshi, M.K.: Improving adversarial robustness of ensembles with diversity training. arXiv preprint arXiv:1901.09981 (2019)"},{"key":"30_CR11","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: Proceedings of the 5th International Conference on Learning Representations (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"30_CR13","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial machine learning at scale. In: Proceedings of the 5th International Conference on Learning Representations (2017)"},{"issue":"7553","key":"30_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"6","key":"30_CR15","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/3477.809027","volume":"29","author":"Y Liu","year":"1999","unstructured":"Liu, Y., Yao, X.: Simultaneous training of negatively correlated neural networks in an ensemble. IEEE Trans. Syst. Man Cybern. 29(6), 716\u2013725 (1999)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"4","key":"30_CR16","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/4235.887237","volume":"4","author":"Y Liu","year":"2000","unstructured":"Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Trans. Evol. Comput. 4(4), 380\u2013387 (2000)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"30_CR17","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: Proceedings of the 6th International Conference on Learning Representations (2018)"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Meng, D., Chen, H.: MagNet: a two-pronged defense against adversarial examples. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 135\u2013147. ACM (2017)","DOI":"10.1145\/3133956.3134057"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2574\u20132582. IEEE (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"30_CR20","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1613\/jair.614","volume":"11","author":"DW Opitz","year":"1999","unstructured":"Opitz, D.W., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169\u2013198 (1999)","journal-title":"J. Artif. Intell. Res."},{"key":"30_CR21","unstructured":"Pang, T., Xu, K., Du, C., Chen, N., Zhu, J.: Improving adversarial robustness via promoting ensemble diversity. In: Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 4970\u20134979. PMLR (2019)"},{"key":"30_CR22","unstructured":"Quinlan, J.R.: Bagging, boosting, and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, pp. 725\u2013730. AAAI (1996)"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan, S., Jain, A., Chellappa, R., Lim, S.N.: Regularizing deep networks using efficient layerwise adversarial training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, pp. 4008\u20134015. AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11688"},{"key":"30_CR24","unstructured":"Sinha, A., Chen, Z., Badrinarayanan, V., Rabinovich, A.: Gradient adversarial training of neural networks. arXiv preprint arXiv:1806.08028 (2018)"},{"key":"30_CR25","unstructured":"Strauss, T., Hanselmann, M., Junginger, A., Ulmer, H.: Ensemble methods as a defense to adversarial perturbations against deep neural networks. arXiv preprint arXiv:1709.03423 (2017)"},{"key":"30_CR26","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: Proceedings of the 2nd International Conference on Learning Representations (2014)"},{"key":"30_CR27","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Goodfellow, I.J., Boneh, D., McDaniel, P.D.: Ensemble adversarial training: attacks and defenses. In: Proceedings of the 6th International Conference on Learning Representations (2018)"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, H., Yao, X.: Negative correlation learning for classification ensembles. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1\u20138. IEEE (2010)","DOI":"10.1109\/IJCNN.2010.5596702"},{"key":"30_CR29","unstructured":"Wu, D., Wang, Y., Xia, S., Bailey, J., Ma, X.: Skip connections matter: On the transferability of adversarial examples generated with resnets. In: Proceedings of the 8th International Conference on Learning Representations (2020)"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Xu, W., Evans, D., Qi, Y.: Feature squeezing: detecting adversarial examples in deep neural networks. In: Proceedings of the 25th Annual Network and Distributed System Security Symposium. The Internet Society (2018)","DOI":"10.14722\/ndss.2018.23198"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 7444\u20137452. AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"30_CR32","unstructured":"Zhao, P., Fu, Z., Wu, O., Hu, Q., Wang, J.: Detecting adversarial examples via key-based network. arXiv preprint arXiv:1806.00580 (2018)"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8991-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T08:16:43Z","timestamp":1674029803000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8991-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811989902","9789811989919"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8991-9_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2022","order":10,"name":"conference_id","label":"Conference ID","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","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":"62","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":"46% - 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":"2.8","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":"2-3","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)"}}]}}