{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:10:22Z","timestamp":1767323422485,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":50,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557363","type":"print"},{"value":"9789819557370","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-5737-0_10","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:07:37Z","timestamp":1767323257000},"page":"133-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Information-Theoretic Point Cloud Defense: Harnessing Conditional Mutual Information Against Adversarial Attacks"],"prefix":"10.1007","author":[{"given":"Kaiwen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhao","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuoyang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teko","family":"Ranoka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shutao","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"10_CR1","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. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"10_CR2","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"4436","DOI":"10.1109\/TIP.2021.3072214","volume":"30","author":"S Cheng","year":"2021","unstructured":"Cheng, S., Chen, X., He, X., Liu, Z., Bai, X.: PRA-Net: point relation-aware network for 3D point cloud analysis. IEEE Trans. Image Process. 30, 4436\u20134448 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Cui, J., Liu, S., Wang, L., Jia, J.: Learnable boundary guided adversarial training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15721\u201315730 (2021)","DOI":"10.1109\/ICCV48922.2021.01543"},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.1109\/TIP.2022.3149225","volume":"31","author":"Q Deng","year":"2022","unstructured":"Deng, Q., Zhang, S., Ding, Z.: An efficient hypergraph approach to robust point cloud resampling. IEEE Trans. Image Process. 31, 1924\u20131937 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Dong, X., et al.: Self-robust 3D point recognition via gather-vector guidance. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11513\u201311521. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01153"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"10_CR8","unstructured":"Fang, H., et al.: One perturbation is enough: on generating universal adversarial perturbations against vision-language pre-training models. arXiv preprint arXiv:2406.05491 (2024)"},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/TIP.2020.3031371","volume":"30","author":"M Feng","year":"2020","unstructured":"Feng, M., Gilani, S.Z., Wang, Y., Zhang, L., Mian, A.: Relation graph network for 3D object detection in point clouds. IEEE Trans. Image Process. 30, 92\u2013107 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR10","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"10_CR11","unstructured":"Goyal, A., Law, H., Liu, B., Newell, A., Deng, J.: Revisiting point cloud shape classification with a simple and effective baseline. In: International Conference on Machine Learning, pp. 3809\u20133820. PMLR (2021)"},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"5072","DOI":"10.1109\/TIP.2021.3078109","volume":"30","author":"J Guo","year":"2021","unstructured":"Guo, J., et al.: Efficient center voting for object detection and 6D pose estimation in 3D point cloud. IEEE Trans. Image Process. 30, 5072\u20135084 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-58610-2_15","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Hamdi","year":"2020","unstructured":"Hamdi, A., Rojas, S., Thabet, A., Ghanem, B.: AdvPC: transferable adversarial perturbations on 3D point clouds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 241\u2013257. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_15"},{"key":"10_CR14","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, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Huang, Q., et al.: Improving adversarial robustness of masked autoencoders via test-time frequency-domain prompting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1600\u20131610 (2023)","DOI":"10.1109\/ICCV51070.2023.00154"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Huang, Q., et al.: Diversity-aware meta visual prompting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10878\u201310887 (2023)","DOI":"10.1109\/CVPR52729.2023.01047"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Huang, Q., Dong, X., Chen, D., Zhou, H., Zhang, W., Yu, N.: Shape-invariant 3D adversarial point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15335\u201315344 (2022)","DOI":"10.1109\/CVPR52688.2022.01490"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Huang, Q., et al.: PointCAT: contrastive adversarial training for robust point cloud recognition. IEEE Trans. Image. Proc. (2024)","DOI":"10.1109\/TIP.2024.3372456"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Huang, Q., Zhang, J., Zhou, W., Zhang, W., Yu, N.: Initiative defense against facial manipulation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 1619\u20131627 (2021)","DOI":"10.1609\/aaai.v35i2.16254"},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/TIP.2021.3136714","volume":"31","author":"L Hui","year":"2022","unstructured":"Hui, L., Cheng, M., Xie, J., Yang, J., Cheng, M.M.: Efficient 3D point cloud feature learning for large-scale place recognition. IEEE Trans. Image Process. 31, 1258\u20131270 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"5769","DOI":"10.1109\/TIP.2021.3082317","volume":"30","author":"A Liu","year":"2021","unstructured":"Liu, A., Liu, X., Yu, H., Zhang, C., Liu, Q., Tao, D.: Training robust deep neural networks via adversarial noise propagation. IEEE Trans. Image Process. 30, 5769\u20135781 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10_CR22","first-page":"4727","volume":"45","author":"D Liu","year":"2022","unstructured":"Liu, D., Hu, W.: Imperceptible transfer attack and defense on 3D point cloud classification. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4727\u20134746 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR23","doi-asserted-by":"publisher","first-page":"4050","DOI":"10.1109\/TIP.2022.3180210","volume":"31","author":"H Liu","year":"2022","unstructured":"Liu, H., Liu, H., Wang, Y., Sun, F., Huang, W.: Fine-grained multilevel fusion for anti-occlusion monocular 3D object detection. IEEE Trans. Image Process. 31, 4050\u20134061 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR24","doi-asserted-by":"publisher","first-page":"4922","DOI":"10.1109\/TIP.2022.3190209","volume":"31","author":"R Liu","year":"2022","unstructured":"Liu, R., Jiang, Z., Yang, S., Fan, X.: Twin adversarial contrastive learning for underwater image enhancement and beyond. IEEE Trans. Image Process. 31, 4922\u20134936 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR25","unstructured":"Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: a simple residual MLP framework. arXiv preprint arXiv:2202.07123 (2022)"},{"key":"10_CR26","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)"},{"key":"10_CR27","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNET: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"10_CR28","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNET++: deep hierarchical feature learning on point sets in a metric space. Adv. Neural. Inf. Process. Syst. 30 (2017)"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Ran, H., Liu, J., Wang, C.: Surface representation for point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18942\u201318952 (2022)","DOI":"10.1109\/CVPR52688.2022.01837"},{"key":"10_CR30","unstructured":"Sun, J., Cao, Y., Chen, Q.A., Mao, Z.M.: Towards robust $$\\{$$LiDAR-based$$\\}$$ perception in autonomous driving: general black-box adversarial sensor attack and countermeasures. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 877\u2013894 (2020)"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Sun, S., et al.: RobNAS: Robust neural architecture search for point cloud adversarial defense. In: 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2025, pp.\u00a01\u20135. IEEE (2025)","DOI":"10.1109\/ICASSP49660.2025.10890087"},{"key":"10_CR32","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"10_CR33","first-page":"1633","volume":"33","author":"F Tramer","year":"2020","unstructured":"Tramer, F., Carlini, N., Brendel, W., Madry, A.: On adaptive attacks to adversarial example defenses. Adv. Neural. Inf. Process. Syst. 33, 1633\u20131645 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR34","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P.: Ensemble adversarial training: attacks and defenses. arXiv preprint arXiv:1705.07204 (2017)"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Tu, J., et al.: Physically realizable adversarial examples for LiDAR object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13716\u201313725 (2020)","DOI":"10.1109\/CVPR42600.2020.01373"},{"key":"10_CR36","doi-asserted-by":"publisher","first-page":"7364","DOI":"10.1109\/TIP.2021.3092818","volume":"30","author":"F Wang","year":"2021","unstructured":"Wang, F., Li, W., Xu, D.: Cross-dataset point cloud recognition using deep-shallow domain adaptation network. IEEE Trans. Image Process. 30, 7364\u20137377 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"5","key":"10_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (ToG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (ToG)"},{"issue":"6","key":"10_CR38","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.1109\/TPAMI.2020.3044712","volume":"44","author":"Y Wen","year":"2020","unstructured":"Wen, Y., Lin, J., Chen, K., Chen, C.P., Jia, K.: Geometry-aware generation of adversarial point clouds. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 2984\u20132999 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR39","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912\u20131920 (2015)"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Xiang, C., Qi, C.R., Li, B.: Generating 3D adversarial point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9136\u20139144 (2019)","DOI":"10.1109\/CVPR.2019.00935"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Xiang, T., Zhang, C., Song, Y., Yu, J., Cai, W.: Walk in the cloud: learning curves for point clouds shape analysis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 915\u2013924 (2021)","DOI":"10.1109\/ICCV48922.2021.00095"},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Xie, C., et al.: Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2730\u20132739 (2019)","DOI":"10.1109\/CVPR.2019.00284"},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Xu, M., Ding, R., Zhao, H., Qi, X.: PAconv: position adaptive convolution with dynamic kernel assembling on point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3173\u20133182 (2021)","DOI":"10.1109\/CVPR46437.2021.00319"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Yang, E.H., Hamidi, S.M., Ye, L., Tan, R., Yang, B.: Conditional mutual information constrained deep learning for classification. IEEE Trans. Neural. Netw. Learn. Syst. (2025)","DOI":"10.1109\/TNNLS.2025.3540014"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Yi, L., et al.: A scalable active framework for region annotation in 3D shape collections. ACM Trans. Graph. (ToG) 35(6), 1\u201312 (2016)","DOI":"10.1145\/2980179.2980238"},{"key":"10_CR46","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. PMLR (2019)"},{"key":"10_CR47","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1109\/TIP.2020.3042088","volume":"30","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Cui, S., Ding, Z.: Hypergraph spectral analysis and processing in 3D point cloud. IEEE Trans. Image Process. 30, 1193\u20131206 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR48","unstructured":"Zhong, X., Chen, B., Fang, H., Gu, X., Xia, S.T., Yang, E.H.: Going beyond feature similarity: effective dataset distillation based on class-aware conditional mutual information. arXiv preprint arXiv:2412.09945 (2024)"},{"key":"10_CR49","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: LG-GAN: label guided adversarial network for flexible targeted attack of point cloud based deep networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10356\u201310365 (2020)","DOI":"10.1109\/CVPR42600.2020.01037"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., Yu, N.: DUP-Net: denoiser and upsampler network for 3D adversarial point clouds defense. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1961\u20131970 (2019)","DOI":"10.1109\/ICCV.2019.00205"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5737-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T03:07:41Z","timestamp":1767323261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5737-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557363","9789819557370"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5737-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}