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Deep Neural Watermarking for Robust Copyright Protection in 3D Point Clouds




TekijätZaman, Khandoker Ashik Uz; Alam, Mohammad Zahangir; Ali, Mohammed N. M.; Miraz, Mahdi H.

KustantajaInternational Association for Educators and Researchers (IAER)

Julkaisuvuosi2025

Lehti: Annals of Emerging Technologies in Computing

Vuosikerta9

Numero4

Aloitussivu17

Lopetussivu30

ISSN2516-0281

eISSN2516-029X

DOIhttps://doi.org/10.33166/AETiC.2025.04.002

Julkaisun avoimuus kirjaamishetkelläAvoimesti saatavilla

Julkaisukanavan avoimuus Kokonaan avoin julkaisukanava

Verkko-osoitehttps://doi.org/10.33166/aetic.2025.04.002

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/506458347


Tiivistelmä

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. Through the integration of conventional spectral methods and modern neural architectures, our hybrid approach establishes a new standard for robust and reliable copyright protection in 3D digital environments. Our work provides a promising approach to intellectual property protection in the growing 3D media sector, meeting crucial demands in gaming, virtual reality, medical imaging and digital content creation.


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