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Separable Reversible Data Hiding Based on Integer Mapping and Multi-MSB Prediction for Encrypted 3D Mesh Models

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

Extensive research has been conducted on image-based reversible data hiding in encrypted domain (RDH-ED) methods, but these methods cannot be directly applied to other cover medium, such as text, audio, video, and 3D mesh. With the widespread use of 3D mesh on the Internet, the use of 3D mesh as cover medium for RDH has gradually become a research topic. The main challenge of studying RDH based on 3D mesh is that the data structure of 3D mesh is complex and the geometric structure is irregular. In this paper, we propose a separable RDH-ED method based on integer mapping and multiple most significant bit (Multi-MSB) prediction. Firstly, all vertices of 3D mesh are divided into “embedded” set and “reference” set, and floating-point vertex values are mapped to integers. Then, sender calculates prediction error of the “embedded” set. Data hider embeds additional data by replacing the Multi-MSB of the encrypted vertex coordinates of the “embedded” set without prediction error. According to different permissions, legal recipients can obtain the original mesh, the additional data or both of them by using the proposed separable method. Experimental results prove that the proposed method outperforms state-of-the-art methods.

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Notes

  1. 1.

    http://shape.cs.princeton.edu/benchmark/index.cgi.

  2. 2.

    http://graphics.stanford.edu/data/3Dscanrep/.

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Acknowledgment

This research work is partly supported by National Natural Science Foundation of China (61872003, 61860206004).

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Correspondence to Zhaoxia Yin .

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Yin, Z., Xu, N., Wang, F., Cheng, L., Luo, B. (2021). Separable Reversible Data Hiding Based on Integer Mapping and Multi-MSB Prediction for Encrypted 3D Mesh Models. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_28

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88006-4

  • Online ISBN: 978-3-030-88007-1

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