标题： 基于笼子的变形用于可迁移且不可防御的点云攻击 显示英文标题

标题： Cage-Based Deformation for Transferable and Undefendable Point Cloud Attack

摘要： 对抗性攻击在点云上通常施加严格的几何约束以保持合理性；然而，这些约束本质上限制了可转移性和不可防御性。 虽然变形提供了一种替代方法，但现有的无结构方法可能会引入不自然的扭曲，使对抗性点云变得显眼，从而削弱其合理性。 在本文中，我们提出了CageAttack，一种基于笼子的变形框架，能够生成自然的对抗性点云。 它首先在目标物体周围构建一个笼子，为平滑、自然的变形提供结构基础。 然后对笼子顶点施加扰动，这些扰动无缝传播到点云，确保生成的变形保持在物体内部并保持合理性。 在三个数据集上的七个3D深度神经网络分类器的大量实验表明，CageAttack在可转移性、不可防御性和合理性之间取得了优越的平衡，优于最先进的方法。 代码将在接受后公开。 显示英文摘要

摘要： Adversarial attacks on point clouds often impose strict geometric constraints to preserve plausibility; however, such constraints inherently limit transferability and undefendability. While deformation offers an alternative, existing unstructured approaches may introduce unnatural distortions, making adversarial point clouds conspicuous and undermining their plausibility. In this paper, we propose CageAttack, a cage-based deformation framework that produces natural adversarial point clouds. It first constructs a cage around the target object, providing a structured basis for smooth, natural-looking deformation. Perturbations are then applied to the cage vertices, which seamlessly propagate to the point cloud, ensuring that the resulting deformations remain intrinsic to the object and preserve plausibility. Extensive experiments on seven 3D deep neural network classifiers across three datasets show that CageAttack achieves a superior balance among transferability, undefendability, and plausibility, outperforming state-of-the-art methods. Codes will be made public upon acceptance.