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Information Hiding Algorithm of 3D Model Multi-carrier Based on Included Normal Angle

Published: 21 December 2023 Publication History

Abstract

To overcome the limitations of current single-carrier 3D model information hiding algorithms, which are constrained by the number of carriers, a multi-carrier information hiding algorithm based on normal angles of 3D models is proposed in this study. The algorithm follows a four-step approach. Firstly, the 3D model is transformed into an affine invariant space to normalize and preprocess it. Secondly, the carrier set of the 3D model is classified based on the normal angle histogram, and the carrier is encoded according to the type of triangular grid. Thirdly, the local entropy of the vertex normal angle is used to label and classify the 3D model, and vertices with varying energies are obtained. Finally, the secret information is embedded in multiple 3D models based on the matching optimization relationship between secret information and carrier information. In order to improve the invisibility of the algorithm, angles with small changes are preferred for modification during the embedding of secret information. The experimental results demonstrate that compared to single-carrier and fusion-state-based 3D model information hiding algorithms, the proposed algorithm has significantly improved invisibility and robustness and can effectively resist Laplace steganalysis.

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  1. Information Hiding Algorithm of 3D Model Multi-carrier Based on Included Normal Angle

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    CSAE '23: Proceedings of the 7th International Conference on Computer Science and Application Engineering
    October 2023
    358 pages
    ISBN:9798400700590
    DOI:10.1145/3627915
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 December 2023

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    Author Tags

    1. 3D Models
    2. Information Hiding
    3. Local Entropy

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    Overall Acceptance Rate 368 of 770 submissions, 48%

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