To prevent the mischievous use of synthetic (fake) point clouds produced by generative models, we pioneer the study of detecting point cloud authenticity and attributing them to their sources. We propose an attribution framework, FAKEPCD, to attribute (fake) point clouds to their respective generative models (or real-world collections). The main idea of FAKEPCD is to train an attribution model that learns the point cloud features from different sources and further differentiates these sources using an attribution signal. Depending on the characteristics of the training point clouds, namely, sources and shapes, we formulate four attribution scenarios: close-world, open-world, single-shape, and multiple-shape, and evaluate FAKEPCD's performance in each scenario. Extensive experimental results demonstrate the effectiveness of FAKEPCD on source attribution across different scenarios. Take the open-world attribution as an example, FAKEPCD attributes point clouds to known sources with an accuracy of 0.82-0.98 and to unknown sources with an accuracy of 0.73-1.00. Additionally, we introduce an approach to visualize unique patterns (fingerprints) in point clouds associated with each source. This explains how FAKEPCD recognizes point clouds from various sources by focusing on distinct areas within them. Overall, we hope our study establishes a baseline for the source attribution of (fake) point clouds.

翻译：为防范生成模型产生的合成（假）点云被恶意使用，我们率先开展了点云真实性检测及源归因研究。我们提出名为FAKEPCD的归因框架，用于将（假）点云归因至其对应的生成模型（或真实世界采集集）。FAKEPCD的核心思想是训练一个归因模型，该模型学习不同来源的点云特征，并进一步通过归因信号区分这些来源。根据训练点云的特性（即来源与形状），我们构建了四种归因场景：封闭世界、开放世界、单形状与多形状，并评估了FAKEPCD在各场景下的性能。大量实验结果表明，FAKEPCD在不同场景下均能有效进行源归因。以开放世界归因为例，FAKEPCD对已知来源点云的归因准确率达0.82-0.98，对未知来源点云的归因准确率达0.73-1.00。此外，我们引入了一种方法用于可视化每个来源点云中的独特模式（指纹），从而解释FAKEPCD如何通过关注点云中的特定区域来识别不同来源的点云。总体而言，我们希望本研究可为（假）点云的源归因建立基准。