{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T15:07:05Z","timestamp":1743779225959},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.<\/jats:p>","DOI":"10.1609\/aaai.v37i2.25338","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T16:14:01Z","timestamp":1687882441000},"page":"2420-2428","source":"Crossref","is-referenced-by-count":9,"title":["Deep Manifold Attack on Point Clouds via Parameter Plane Stretching"],"prefix":"10.1609","volume":"37","author":[{"given":"Keke","family":"Tang","sequence":"first","affiliation":[]},{"given":"Jianpeng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Weilong","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yawen","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Song","sequence":"additional","affiliation":[]},{"given":"Zhaoquan","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Zhihong","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Wenping","family":"Wang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2023,6,26]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/25338\/25110","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/25338\/25110","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T16:14:01Z","timestamp":1687882441000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/25338"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,6,27]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v37i2.25338","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2023,6,26]]}}}