The protection of intellectual property has become critical due to the rapid growth of three-dimensional content in digital media. Unlike traditional images or videos, 3D point clouds present unique challenges for copyright enforcement, as they are especially vulnerable to a range of geometric and non-geometric attacks that can easily degrade or remove conventional watermark signals. In this paper, we address these challenges by proposing a robust deep neural watermarking framework for 3D point cloud copyright protection and ownership verification. Our approach embeds binary watermarks into the singular values of 3D point cloud blocks using spectral decomposition, i.e. Singular Value Decomposition (SVD), and leverages the extraction capabilities of Deep Learning using PointNet++ neural network architecture. The network is trained to reliably extract watermarks even after the data undergoes various attacks such as rotation, scaling, noise, cropping and signal distortions. We validated our method using the publicly available ModelNet40 dataset, demonstrating that deep learning-based extraction significantly outperforms traditional SVD-based techniques under challenging conditions. Our experimental evaluation demonstrates that the deep learning-based extraction approach significantly outperforms existing SVD-based methods with deep learning achieving bitwise accuracy up to 0.83 and Intersection over Union (IoU) of 0.80, compared to SVD achieving a bitwise accuracy of 0.58 and IoU of 0.26 for the Crop (70%) attack, which is the most severe geometric distortion in our experiment. This demonstrates our method's ability to achieve superior watermark recovery and maintain high fidelity even under severe distortions.

翻译：随着数字媒体中三维内容的快速增长，知识产权保护变得至关重要。与传统图像或视频不同，三维点云在版权保护方面面临独特挑战，因为它们特别容易受到一系列几何和非几何攻击的影响，这些攻击可能轻易破坏或移除传统水印信号。本文针对这些挑战，提出了一种鲁棒的深度神经网络水印框架，用于三维点云版权保护与所有权验证。我们的方法通过谱分解（即奇异值分解，SVD）将二进制水印嵌入三维点云块的奇异值中，并利用基于PointNet++神经网络架构的深度学习提取能力。该网络经过训练，即使在数据遭受旋转、缩放、噪声、裁剪和信号失真等多种攻击后，仍能可靠地提取水印。我们在公开可用的ModelNet40数据集上验证了该方法，结果表明在挑战性条件下，基于深度学习的提取方法显著优于传统的基于SVD的技术。实验评估表明，基于深度学习的提取方法明显优于现有基于SVD的方法：在实验中最为严重的几何失真——裁剪（70%）攻击下，深度学习实现了高达0.83的比特精度和0.80的交并比（IoU），而SVD仅达到0.58的比特精度和0.26的IoU。这证明了我们的方法即使在严重失真条件下，仍能实现优异的水印恢复能力并保持高保真度。