{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:50:53Z","timestamp":1771458653808,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045454","type":"print"},{"value":"9783032045461","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"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\/978-3-032-04546-1_33","type":"book-chapter","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:52:41Z","timestamp":1757515961000},"page":"401-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving the\u00a0Transferability of\u00a0Point Cloud Attack via\u00a0Spectral-Aware Admix and\u00a0Optimization Designs"],"prefix":"10.1007","author":[{"given":"Shiyu","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daizong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"33_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 (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907\u20131915 (2017)","DOI":"10.1109\/CVPR.2017.691"},{"key":"33_CR3","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 (CVPR), pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"33_CR4","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"33_CR5","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":"33_CR6","doi-asserted-by":"crossref","unstructured":"Hu, Q., Liu, D., Hu, W.: Exploring the devil in graph spectral domain for 3D point cloud attacks (2022)","DOI":"10.1007\/978-3-031-20062-5_14"},{"key":"33_CR7","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1109\/TMM.2021.3111440","volume":"24","author":"W Hu","year":"2022","unstructured":"Hu, W., Pang, J., Liu, X., Tian, D., Lin, C.W., Vetro, A.: Graph signal processing for geometric data and beyond: theory and applications. IEEE Trans. Multimed. 24, 3961\u20133977 (2022). https:\/\/doi.org\/10.1109\/TMM.2021.3111440","journal-title":"IEEE Trans. Multimed."},{"key":"33_CR8","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":"33_CR9","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2017)"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Liu, D., Hu, W.: Imperceptible transfer attack and defense on 3D point cloud classification. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3193449"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Long, Y., et al.: Frequency domain model augmentation for adversarial attack (2022)","DOI":"10.1007\/978-3-031-19772-7_32"},{"key":"33_CR12","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":"33_CR13","unstructured":"Mahalanobis, P.C.: On the generalized distance in statistics (1936). https:\/\/api.semanticscholar.org\/CorpusID:117765088"},{"key":"33_CR14","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 (CVPR), pp. 652\u2013660 (2017)"},{"key":"33_CR15","doi-asserted-by":"publisher","unstructured":"Sandryhaila, A., Moura, J.M.F.: Discrete signal processing on graphs: graph Fourier transform. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6167\u20136170 (2013). https:\/\/doi.org\/10.1109\/ICASSP.2013.6638850","DOI":"10.1109\/ICASSP.2013.6638850"},{"issue":"18","key":"33_CR16","doi-asserted-by":"publisher","first-page":"5097","DOI":"10.3390\/s20185097","volume":"20","author":"SP Singh","year":"2020","unstructured":"Singh, S.P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., Guly\u00e1s, B.: 3D deep learning on medical images: a review. Sensors 20(18), 5097 (2020)","journal-title":"Sensors"},{"key":"33_CR17","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Tu, C.C., et al.: Autozoom: autoencoder-based zeroth order optimization method for attacking black-box neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 742\u2013749 (2019)","DOI":"10.1609\/aaai.v33i01.3301742"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., He, X., Wang, J., He, K.: Admix: enhancing the transferability of adversarial attacks (2021)","DOI":"10.1109\/ICCV48922.2021.01585"},{"issue":"5","key":"33_CR20","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)"},{"key":"33_CR21","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. (TPAMI) (2020)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L.: Pointconv: deep convolutional networks on 3D point clouds (2020)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"33_CR23","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 (CVPR), pp. 1912\u20131920 (2015)"},{"key":"33_CR24","unstructured":"Wu, Z., Duan, Y., Wang, H., Fan, Q., Guibas, L.J.: If-defense: 3D adversarial point cloud defense via implicit function based restoration. arXiv preprint arXiv:2010.05272 (2020)"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Xiang, C., Qi, C.R., Li, B.: Generating 3D adversarial point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9136\u20139144 (2019)","DOI":"10.1109\/CVPR.2019.00935"},{"key":"33_CR26","unstructured":"Yang, J., Zhang, Q., Fang, R., Ni, B., Liu, J., Tian, Q.: Adversarial attack and defense on point sets (2021)"},{"key":"33_CR27","doi-asserted-by":"publisher","unstructured":"Yang, K., Lin, X.Y., Sun, Y., Ho, T.Y., Jin, Y.: 3D-adv: black-box adversarial attacks against deep learning models through 3D sensors. In: 2021 58th ACM\/IEEE Design Automation Conference (DAC), pp. 547\u2013552 (2021). https:\/\/doi.org\/10.1109\/DAC18074.2021.9586275","DOI":"10.1109\/DAC18074.2021.9586275"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Yu, T., Meng, J., Yuan, J.: Multi-view harmonized bilinear network for 3D object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 186\u2013194 (2018)","DOI":"10.1109\/CVPR.2018.00027"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Improving the transferability of adversarial samples by path-augmented method (2023)","DOI":"10.1109\/CVPR52729.2023.00790"},{"key":"33_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120245","volume":"662","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Dong, Y., Zhu, J., Zhu, J., Kuang, M., Yuan, X.: Improving transferability of 3D adversarial attacks with scale and shear transformations. Inf. Sci. 662, 120245 (2024)","journal-title":"Inf. Sci."},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, T., Chen, C., Yuan, J., Li, B., Ren, K.: Pointcloud saliency maps. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1598\u20131606 (2019)","DOI":"10.1109\/ICCV.2019.00168"},{"key":"33_CR32","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 International Conference on Computer Vision (ICCV), pp. 1961\u20131970 (2019)","DOI":"10.1109\/ICCV.2019.00205"},{"key":"33_CR33","doi-asserted-by":"publisher","unstructured":"Zhu, H., Ren, Y., Sui, X., Yang, L., Jiang, W.: Boosting adversarial transferability via gradient relevance attack. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4718\u20134727 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.00437","DOI":"10.1109\/ICCV51070.2023.00437"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04546-1_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T14:52:49Z","timestamp":1757515969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04546-1_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"ISBN":["9783032045454","9783032045461"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04546-1_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"11 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","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":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}