Abstract
With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold’s cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack.
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References
Bogdanov, A., Khovratovich, D., & Rechberger, C. (2011). Biclique cryptanalysis of the full AES. In Proceedings of the 17th International Conference on the Theory and Application of Cryptology and Information Security (pp. 344–371). Berlin: Springer.
Éluard, M., Maetz, Y., & Doërr, G. (2013). Geometry-preserving encryption for 3D meshes. In Actes de Compression et REprsentation des Signaux Audiovisuels (pp. 7–12).
Jin, X., Chen, Y., Ge, S., Zhang, K., Li, X., Li, Y., et al. (2015). Color image encryption in CIE L*a*b* space. In International Conference on Applications and Techniques in Information Security (pp. 74–85). Berlin: Springer.
Jin, X., Tian, Y., Song, C., Wei, G., Li, X., Zhao, G., et al. (2015). An invertible and anti-chosen plaintext attack image encryption method based on DNA encoding and chaotic mapping. In 2015 Chinese Automation Congress (CAC) (pp. 1159–1164). Piscataway: IEEE.
Jin, X., Wu, Z., Song, C., Zhang, C., & Li, X. (2016). 3D point cloud encryption through chaotic mapping. In Advances in Multimedia Information Processing (PCM) 2016. Proceedings of the 17th Pacific-rim Conference on Multimedia (pp. 119–129). Cham: Springer.
Jin, X., Yin, S., Li, X., Zhao, G., Tian, Z., Sun, N., et al. (2016). Color image encryption in YCbCr space. In 8th International Conference on Wireless Communications & Signal Processing, WCSP (pp. 1–5). Piscataway: IEEE.
Jin, X., Zhu, S., Xiao, C., Sun, H., Li, X., Zhao, G., et al. (2017). 3D textured model encryption via 3D Lu chaotic mapping. Science China Information Sciences, 60, 122107.
Jin, X., Yin, S., Liu, N., Li, X., Zhao, G., & Ge, S. (2018). Color image encryption in non-RGB color spaces. Multimedia Tools and Applications, 77, 15851–15873.
Jolfaei, A., Wu, X., & Muthukkumarasamy, V. (2015). A 3D object encryption scheme which maintains dimensional and spatial stability. IEEE Transactions on Information Forensics and Security, 10(2), 409–422.
Jolfaei, A., Wu, X., & Muthukkumarasamy, V. (2016). A secure lightweight texture encryption scheme. In Image and Video Technology – PSIVT 2015 Workshops. PSIVT 2015 (pp. 344–356). Cham: Springer
del Rey, Á. M. (2015). A method to encrypt 3D solid objects based on three-dimensional cellular automata. In Hybrid Artificial Intelligent Systems. 10th International Conference on Hybrid Artificial Intelligence Systems (pp. 427–438). Cham: Springer
Shannon, C. (1949). Communication theory of secrecy systems. Bell System Technical Journal, 28, 656–715.
Verma, O. P., Nizam, M., & Ahmad, M. (2013). Modified multi-chaotic systems that are based on pixel shuffle for image encryption. Journal of Information Processing Systems, 9(2), 271–286.
Zhen, P., Zhao, G., Min, L., & Jin, X. (2016). Chaos-based image encryption scheme combining DNA coding and entropy. Multimedia Tools and Applications, 75(11), 6303–6319.
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61772047, 61772513), the Science and Technology Project of the State Archives Administrator (Grant No. 2015-B-10), and the Fundamental Research Funds for the Central Universities (Grant No. 328201803, 328201801).
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Jin, X. et al. (2020). Multi-Level Chaotic Maps for 3D Textured Model Encryption. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_10
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DOI: https://doi.org/10.1007/978-3-030-17763-8_10
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