A Dual‐Protection Framework for 3D Point Clouds: Robust Watermarking via RDWT‐SVD and High‐Capacity Steganography With Incremental Point Clouds.
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| Title: | A Dual‐Protection Framework for 3D Point Clouds: Robust Watermarking via RDWT‐SVD and High‐Capacity Steganography With Incremental Point Clouds. |
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| Authors: | Wang, Hui1 (AUTHOR), Qin, Ling2 (AUTHOR) 5101042@wxsc.edu.cn, Sequenzia, Gaetano (AUTHOR) gsequenzia@dii.unict.it |
| Source: | Modelling & Simulation in Engineering. 5/7/2026, Vol. 2026, p1-17. 17p. |
| Subjects: | Point cloud, Digital watermarking |
| Abstract: | 3D point cloud models are being utilized more and more in computer vision, virtual reality, intelligent manufacturing, and cultural heritage preservation as a result of the quick advancements in computer graphics and 3D modeling technologies. Therefore, protecting the copyright and data integrity of 3D models has become an important issue. The study enhances the robustness of the model to rotation, translation, and scaling attacks by affine invariant processing. Moreover, the digital watermark is embedded into the processed 2D image by combining techniques such as principal component analysis, coordinate projection, redundant discrete wavelet transform, and singular value decomposition. The experimental results show that the peak signal‐to‐noise ratios of the four 3D models after embedding the watermark are all higher than 47 dB, among which the Happy_recon model reaches 56.83 dB. The incremental 3D point cloud information hiding algorithm achieves an embedding capacity of 1643 bits in the Happy_recon model, which is 57.22% higher than that of the label clustering method. In the watermark robustness test, the bit error rate of this method against geometric attacks is zero, and the normalized cross‐relation number is 1. Under a 40% sheet‐cutting attack, the bit accuracy rate still remained above 0.85. The peak signal‐to‐noise ratio after embedding the watermark is on average approximately 18.2% higher than that of the method based on geometric features and approximately 9.5% higher than that of the method based on the transform domain. The information hiding algorithm based on incremental point cloud improves the embedding capacity by 57.22% compared with the label clustering method and by 29.98% compared with the distance feature method. In terms of robustness, the resistance to geometric attacks is completely immune, and the bit accuracy rate against noise attacks is approximately 5.9% higher than that of deep learning methods. In conclusion, the proposed method effectively enhances the performance of 3D point cloud models in watermarking and information hiding tasks. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | 3D point cloud models are being utilized more and more in computer vision, virtual reality, intelligent manufacturing, and cultural heritage preservation as a result of the quick advancements in computer graphics and 3D modeling technologies. Therefore, protecting the copyright and data integrity of 3D models has become an important issue. The study enhances the robustness of the model to rotation, translation, and scaling attacks by affine invariant processing. Moreover, the digital watermark is embedded into the processed 2D image by combining techniques such as principal component analysis, coordinate projection, redundant discrete wavelet transform, and singular value decomposition. The experimental results show that the peak signal‐to‐noise ratios of the four 3D models after embedding the watermark are all higher than 47 dB, among which the Happy_recon model reaches 56.83 dB. The incremental 3D point cloud information hiding algorithm achieves an embedding capacity of 1643 bits in the Happy_recon model, which is 57.22% higher than that of the label clustering method. In the watermark robustness test, the bit error rate of this method against geometric attacks is zero, and the normalized cross‐relation number is 1. Under a 40% sheet‐cutting attack, the bit accuracy rate still remained above 0.85. The peak signal‐to‐noise ratio after embedding the watermark is on average approximately 18.2% higher than that of the method based on geometric features and approximately 9.5% higher than that of the method based on the transform domain. The information hiding algorithm based on incremental point cloud improves the embedding capacity by 57.22% compared with the label clustering method and by 29.98% compared with the distance feature method. In terms of robustness, the resistance to geometric attacks is completely immune, and the bit accuracy rate against noise attacks is approximately 5.9% higher than that of deep learning methods. In conclusion, the proposed method effectively enhances the performance of 3D point cloud models in watermarking and information hiding tasks. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 16875591 |
| DOI: | 10.1155/mse/8826343 |
