FAKEPCD: Fake Point Cloud Detection via Source Attribution
Yiting Qu Zhikun Zhang Yun Shen Michael Backes Yang Zhang
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.