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：3D深消费点云模型在计算机视野中取得了健全的应用效果。然而,最近的研究表明,它们很容易受到 3D 对抗点云的影响。在本文中,我们认为这些恶意点云是3D 对抗点云的现有探测技术,我们不依赖受害者3D 深度模型的输出来进行歧视。广泛的实验表明,3D-VFD取得了最先进的检测,并能够有效地探测到基于点加点和点穿透的3D 对抗点云,同时保持快速探测速度。