3D deep models consuming point clouds have achieved sound application effects in computer vision. However, recent studies have shown they are vulnerable to 3D adversarial point clouds. In this paper, we regard these malicious point clouds as 3D steganography examples and present a new perspective, 3D steganalysis, to counter such examples. Specifically, we propose 3D-VFD, a victim-free detector against 3D adversarial point clouds. Its core idea is to capture the discrepancies between residual geometric feature distributions of benign point clouds and adversarial point clouds and map these point clouds to a lower dimensional space where we can efficiently distinguish them. Unlike existing detection techniques against 3D adversarial point clouds, 3D-VFD does not rely on the victim 3D deep model's outputs for discrimination. Extensive experiments demonstrate that 3D-VFD achieves state-of-the-art detection and can effectively detect 3D adversarial attacks based on point adding and point perturbation while keeping fast detection speed.

翻译：消耗点云的3D深度模型在计算机视觉中取得了良好的应用效果。然而，近年研究表明这些模型易受3D对抗性点云的攻击。本文将恶意点云视为3D隐写示例，并提出以3D隐写分析的新视角来对抗此类示例。具体而言，我们提出3D-VFD，一种面向3D对抗性点云的无受害者检测器。其核心思想是捕获良性点云与对抗性点云之间残差几何特征分布的差异，并将这些点云映射至可高效区分的低维空间。与现有面向3D对抗性点云的检测技术不同，3D-VFD不依赖受害者3D深度模型的输出进行判别。大量实验证明，3D-VFD实现了最先进的检测性能，能在保持快速检测速度的同时有效检测基于点添加和点扰动的3D对抗性攻击。