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    Lü Wanli, Tang Yun, Yin Zhaoxia, Luo Bin. Reversible Data Hiding for 3D Mesh Model in Encrypted Domain Based on Vertex Partition and Coordinate Standardization[J]. Journal of Computer Research and Development, 2024, 61(6): 1536-1544. DOI: 10.7544/issn1000-1239.202221040
    Citation: Lü Wanli, Tang Yun, Yin Zhaoxia, Luo Bin. Reversible Data Hiding for 3D Mesh Model in Encrypted Domain Based on Vertex Partition and Coordinate Standardization[J]. Journal of Computer Research and Development, 2024, 61(6): 1536-1544. DOI: 10.7544/issn1000-1239.202221040

    Reversible Data Hiding for 3D Mesh Model in Encrypted Domain Based on Vertex Partition and Coordinate Standardization

    • Reversible data hiding in encrypted domain enables the secure and confidential embedding of additional information in encrypted multimedia, ensuring the privacy and integrity of both the carrier and the embedded data during transmission. The authorized recipients can extract the data without any loss and recover the media successfully. In the realm of digital media, 3D mesh models, being a relatively nascent form, possesses a distinctive file structure markedly different from that of conventional image media. Consequently, limited research has been conducted in this domain. Augmenting the embedding capacity of 3D mesh models in the encrypted domain poses an enduring challenge. The direct application of multiple most significant bit prediction algorithm from the image domain to 3D mesh models is impeded by disparities in data storage formats, thus encumbering the predictive performance of algorithms. To effectively tackle this issue, we propose the adoption of coordinate standardization to eliminate the influence of the sign bit and ameliorate the prediction algorithm’s overall performance. In order to further mitigate the inclusion of redundant auxiliary information, we introduce the integration of the selection of embedding set vertices into our experiments, which effectively generates additional payload space. The experimental results affirm that our purposed methodology attains the maximum embedding capacity while guaranteeing lossless and separable recovery of both the model and the embedded information, surpassing the capabilities of existing techniques.
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