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收稿：2021-05-19，
修回：2021-9-10，
录用：2021-9-17，
纸质出版：2022-01-16
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在计算机图形学中，3D形状可有多种表示形式，包括网格、体素、多视角图像、点云、参数曲面和隐式曲面等。3D网格是常见的表示形式之一，其构成3D物体的顶点、边缘和面的集合，通常用于表示数字3D物体的曲面和容积特性。在过去的20年中，基于3D网格载体的虚拟现实、实时仿真和交叉3维设计已经在工业，医疗和娱乐等场景得到广泛应用，以3D网格为载体的水印技术、隐写和隐写分析技术也受到研究者的关注。相比于图像与音视频等载体的隐写，3D网格具备嵌入方式灵活与载体形式多变等其自身的优势。本文回顾了3D网格隐写和隐写分析的发展，并对现有研究工作进行了系统的总结和分类。根据嵌入方式和嵌入位置将隐写算法分成4类：两态调制隐写、最低位隐写、置换隐写和变换域隐写；根据特征提取角度将隐写分析算法分为2类：通用型隐写分析和专用型隐写分析。随后，介绍了每个类别的技术，综合安全性、鲁棒性、容量以及运算效率分析了各类算法的优劣性，总结当前的发展水平，并提供了不同嵌入率下两种数据集上隐写分析算法之间的性能比较。最后讨论了3D隐写和隐写分析现有技术的局限性，并探讨了潜在的研究方向，旨在为后续学者进一步推动3D隐写和隐写分析技术提供指导。
Three-dimensional (3D) meshes have been mainly used to illustrate virtual surfaces and volumes. 3D meshes have implemented in industrial
medical
and entertainment applications over the past decade
which are of great practical significance for 3D mesh steganography and steganalysis. The application of 3D geometry as host object has been focused over the past few years based on image
audio files and videos processing method in early steganography and steganalysis. Cost effective 3D hardware stimulates the widespread use of 3D meshes in the evolving of the computer aided design(CAD) industry to real-world end-user applications such as virtual reality (VR)
web integration
Facebook support
video games
3D printing and animated movies. Hence
the development of computer graphics has facilitated the production
application and distribution of the emerging generation of 3D geometry digital media. Moreover
the flexible data structure of 3D geometry provides enough space to host security information
making it ideal for use a cover object for steganography. A 3D mesh consists of a set of triangular faces
which is to form an approximation of a real 3D object. A 3D mesh has 3 different synthesized factors: vertices
edges
and faces; a mesh can also be taken as the integration of geometry connectivity
where the geometry provides the 3D positions of all its vertices
and connectivity
which provides the information hidden between different adjacent vertices. A systematic overview of 3D mesh steganography and steganalysis has been issued related to computer graphics and security. The objective projects in the context of the types of steganographic and steganalytic methods have been reviewed in literature. Quantitative evaluation has been conducted from the perspective of security assessment simultaneously. The target of this task is to demonstrate the evaluation procedures in the 3D mesh steganography and steganalysis methods as a whole. It is essential to recognize a growing number of efforts on how to improve the anti-steganalysis efforts in the case of steganographer side and how to improve the steganalysis ability in the case of the steganalyzer side. Some standard evaluation metrics
an overall summary
and an understanding of relevant research results have been evaluated based on the previous analyses. Unlike image steganography which embeds data by modifying pixel values
3D mesh steganography modifies vertex coordinates or vertex order to embed data. In the latest literature analysis of 3D steganography and steganalysis of Girdhar and Kumar's work
steganography is divided into three categories (geometrical domain
topological domain and representation domain)
which reflects the robustness of the algorithms to attacks
and steganalysis is briefly introduced. The entire communities of 3D steganography and steganalysis have to be further promoted. For instance
the geometrical domain can still be divided into two-state domain and the least significant bit(LSB) domain. In addition
the concepts of "steganography" and "watermarking" can be used interchangeably. Watermarking seeks robustness
protects copyright ownership and reduces the counterfeiting of digital multimedia
while steganography seeks un-detectability used for covert communication. They focus has been primarily on analyzing the robustness of the existing methods
while the undetectability of steganography is a more important property because of its practical requirement: covert communication. A more comprehensive survey
a clear taxonomy and several criteria for evaluating robustness and un-detectability has been offered. Conversely
hidden data has been used into reversible data hiding and steganography. For the structure of 3D data
the 3D mesh and RGBD image have been mainly concentrated. 3D meshes as carriers and steganographic techniques have been considered. The steganographic techniques into several domains (two-state domain
LSB domain
permutation domain and transform domain) in a subdivision way have been divided with no small embedding capacities. This demonstration has evolved common digital attacks including affine transform attack
vertex reordering attack
noise addition attack
smoothing attack and simplification attack. In addition
3D mesh steganalysis has been divided into two aspects (general steganalysis and specific steganalysis). For overall steganalysis
there are YANG208 features
local feature set(LFS)52 features
LFS64 features
LFS76 features
LFS124 features
normal voting tensor(NVT)+ features and 3D wavelet feature set(WFS)228 features respectively. Current methods have revealed strong weaknesses and strengths from which we can learn for future work. In order to evaluate the performance of various steganographic and steganalytic methods clearly
it is important to identify standards for users friendly. Meanwhile
the steganographic performance based on three general requirements (i.e.
security
capacity and robustness) has been evaluated. Ensemble learning is an effective way to produce a variety of base classifiers
from which a new classifier with a better performance can be derived
and ensemble classifier is used to evaluate steganalysis performance
a common tool for steganalysis. The datasets proposed are suitable for the princeton segmentation benchmark and the Princeton ModelNet
where the former has 354 objects and the latter has 12 311 mesh objects with 40 categories. Some promising future research directions and challenges in improving the performance of 3D mesh steganography and steganalysis have been highlighted. 3D mesh steganography research has been summarized as following: 1) combining the permutation domain and LSB domain; 2) designing spatial steganographic models; 3) designing steganalysis-resistant permutation steganographic methods; 4) designing 3D mesh batch steganography methods and 5) designing 3D-printing-material-based robust steganography methods. Open issues of 3D mesh steganalysis has been summarized as bellows: 1) rich steganalytic features designation for universal blind steganalysis; 2) designing deep-learning-based steganalysis methods; 3) designing a finer distance metric to improve the steganalysis of permutation steganography and 4) cover source mismatch problem.
Bajaj C L and Xu G L. 2003. Anisotropic diffusion of surfaces and functions on surfaces. ACM Transactions on Graphics, 22(1): 4-32 [DOI: 10.1145/588272.588276]
Bogomjakov A, Gotsman C and Isenburg M. 2008. Distortion-free steganography for polygonal meshes. Computer Graphics Forum, 27(2): 637-642 [DOI: 10.1111/j.1467-8659.2008.01161.x]
Bollobás B. 1998. Modern Graph Theory. New York: Springer-Verlag [DOI: 10.1007/978-1-4612-0619-4]
Bors A G and Luo M. 2013. Optimized 3D watermarking for minimal surface distortion. IEEE Transactions on Image Processing, 22(5): 1822-1835 [DOI: 10.1109/TIP.2012.2236345]
Botsch M, Kobbelt L, Pauly M, Alliez P and Lévy B. 2010. Smoothing//Polygon Mesh Processing. CRC Press: 61-74 [ DOI: 10.1201/b10688-6 http://dx.doi.org/10.1201/b10688-6 ]
Cayre F and Macq B. 2003. Data hiding on 3-D triangle meshes. IEEE Transactions on Signal Processing, 51(4): 939-949 [DOI: 10.1109/tsp.2003.809380]
Chao M W, Lin C H, Yu C W and Lee T Y. 2009. A high capacity 3D steganography algorithm. IEEE Transactions on Visualization and Computer Graphics, 15(2): 274-284 [DOI: 10.1109/tvcg.2008.94]
Chen X B, Golovinskiy A and Funkhouser T. 2009. A benchmark for 3D mesh segmentation. ACM Transactions on Graphics, 28(3): #73 [DOI: 10.1145/1531326.1531379]
Cho J W, Prost R and Jung H Y. 2007. An oblivious watermarking for 3-D polygonal meshes using distribution of vertex norms. IEEE Transactions on Signal Processing, 55(1): 142-155 [DOI: 10.1109/tsp.2006.882111]
Denemark T and Fridrich J. 2015. Improving steganographic security by synchronizing the selection channel//Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security. Portland, USA: ACM: 5-14 [ DOI: 10.1145/2756601.2756620 http://dx.doi.org/10.1145/2756601.2756620 ]
Dyn N, Levine D and Gregory J A. 1990. A butterfly subdivision scheme for surface interpolation with tension control. ACM Transactions on Graphics, 9(2): 160-169 [DOI: 10.1145/78956.78958]
Eigensatz M, Sumner R W and Pauly M. 2008. Curvature-domain shape processing. Computer Graphics Forum, 27(2): 241-250 [DOI: 10.1111/j.1467-8659.2008.01121.x]
Filler T, Judas J and Fridrich J. 2011. Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Transactions on Information Forensics and Security, 6(3): 920-935 [DOI: 10.1109/tifs.2011.2134094]
Fleishman S, Drori I and Cohen-Or D. 2003. Bilateral mesh denoising//ACM SIGGRAPH. San Diego, USA: ACM: 950-953 [ DOI: 10.1145/1201775.882368 http://dx.doi.org/10.1145/1201775.882368 ]
Fridrich J J, Goljan M and Hogea D. 2002. Steganalysis of JPEG images: breaking the F5 algorithm//Proceedings of the 5th International Workshop on Information Hiding. Noordwijkerhout, the Netherlands: Springer: 310-323 [ DOI: 10.1007/3-540-36415-3_20 http://dx.doi.org/10.1007/3-540-36415-3_20 ]
Fridrich J and Kodovský J. 2012. Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 7(3): 868-882 [DOI: 10.1109/tifs.2012.2190402]
Girdhar A and Kumar V. 2018. Comprehensive survey of 3D image steganography techniques. IET Image Processing, 12(1): 1-10 [DOI: 10.1049/iet-ipr.2017.0162]
Hamilton W L, Ying R and Leskovec J. 2017. Inductive representation learning on large graphs//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc. : 1025-1035
Huang N C, Li M T and Wang C M. 2009. Toward optimal embedding capacity for permutation steganography. IEEE Signal Processing Letters, 16(9): 802-805 [DOI: 10.1109/lsp.2009.2024794]
Itier V and Puech W. 2017. High capacity data hiding for 3D point clouds based on static arithmetic coding. Multimedia Tools and Applications, 76(24): 26421-26445 [DOI: 10.1007/s11042-016-4163-y]
Kanai S, Date H and Kishinami T. 1998. Digital watermarking for 3D polygons using multiresolution wavelet decomposition//Proceedings of the 6th International Workshop on Geometric Modeling: Fundamentals and Application. Tokyo, Japan: [s. n.]: 296-307
Ker A D. 2006. Batch steganography and pooled steganalysis//Proceedings of the 8th International Workshop on Information Hiding. Alexandria, USA: Springer: 265-281 [ DOI: 10.1007/978-3-540-74124-4_18 http://dx.doi.org/10.1007/978-3-540-74124-4_18 ]
Ker A D. 2007. A capacity result for batch steganography. IEEE Signal Processing Letters, 14(8): 525-528 [DOI: 10.1109/lsp.2006.891319]
Kim D, Jang H U, Choi H Y, Son J, Yu I J and Lee H K. 2017. Improved 3D mesh steganalysis using homogeneous kernel map//Proceedings of International Conference on Information Science and Applications. Singapore, Singapore: Springer: 358-365 [ DOI: 10.1007/978-981-10-4154-9_42 http://dx.doi.org/10.1007/978-981-10-4154-9_42 ]
KodovskýJ and Fridrich J. 2009. Calibration revisited//Proceedings of the 11th ACM workshop on Multimedia and Security. Princeton, USA: ACM: 63-74 [ DOI: 10.1145/1597817.1597830 http://dx.doi.org/10.1145/1597817.1597830 ]
Kodovský J, Fridrich J and Holub V. 2012. Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security, 7(2): 432-444 [DOI: 10.1109/tifs.2011.2175919]
Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe, USA: Curran Associates Inc. : 1097-1105
Li B, Wang M, Li X L, Tan S Q and Huang J W. 2015. A strategy of clustering modification directions in spatial image steganography. IEEE Transactions on Information Forensics and Security, 10(9): 1905-1917 [DOI: 10.1109/tifs.2015.2434600]
Li N N, Hu J B, Sun R M, Wang S F and Luo Z X. 2017b. A high-capacity 3D steganography algorithm with adjustable distortion. IEEE Access, 5: 24457-24466 [DOI: 10.1109/access.2017.2767072]
Li Z Y, Beugnon S, Puech W and Bors A G. 2017a. Rethinking the high capacity 3D steganography: increasing its resistance to steganalysis//Proceedings of 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE: 510-514 [ DOI: 10.1109/icip.2017.8296333 http://dx.doi.org/10.1109/icip.2017.8296333 ]
Li Z Y and Bors A G. 2016a. 3D mesh steganalysis using local shape features//Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China: IEEE: 2144-2148 [ DOI: 10.1109/icassp.2016.7472056 http://dx.doi.org/10.1109/icassp.2016.7472056 ]
Li Z Y and Bors A G. 2016b. Selection of robust features for the cover source mismatch problem in 3D steganalysis//Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico: IEEE: 4256-4261 [ DOI: 10.1109/icpr.2016.7900302 http://dx.doi.org/10.1109/icpr.2016.7900302 ]
Li Z Y and Bors A G. 2017. Steganalysis of 3D objects using statistics of local feature sets. Information Sciences, 415/416: 85-99 [DOI: 10.1016/j.ins.2017.06.011]
Li Z Y and Bors A G. 2020a. Selection of robust and relevant features for 3-D steganalysis. IEEE Transactions on Cybernetics, 50(5): 1989-2001 [DOI: 10.1109/tcyb.2018.2883082]
Li Z Y and Bors A G. 2020b. Steganalysis of meshes based on 3D wavelet multiresolution analysis. Information Sciences, 522: 164-179 [DOI: 10.1016/j.ins.2020.02.061]
Li Z Y, Gong D F, Liu F L and Bors A G. 2018. 3D steganalysis using the extended local feature set//Proceedings of the 25th IEEE International Conference on Image Processing. Athens, Greece: IEEE: 1683-1687 [ DOI: 10.1109/icip.2018.8451643 http://dx.doi.org/10.1109/icip.2018.8451643 ]
Lounsbery M, DeRose T D and Warren J. 1997. Multiresolution analysis for surfaces of arbitrary topological type. ACM Transactions on Graphics, 16(1): 34-73 [DOI: 10.1145/237748.237750]
PevnýT andFridrich J. 2008. Benchmarking for steganography//Proceedings of the 10th International Workshop on Information Hiding. Santa Barbara, USA: Springer: 251-267 [ DOI: 10.1007/978-3-540-88961-8_18 http://dx.doi.org/10.1007/978-3-540-88961-8_18 ]
Rugis J and Klette R. 2006. A scale invariant surface curvature estimator//Proceedings of the 1st Pacific-Rim Symposium on Image and Video Technology. Hsinchu, China: Springer: 138-147 [ DOI: 10.1007/11949534_14 http://dx.doi.org/10.1007/11949534_14 ]
Simmons G J. 1984. The prisoners' problem and the subliminal channel//Advances in Cryptology. Boston, USA: Springer: 51-67 [ DOI: 10.1007/978-1-4684-4730-9_5 http://dx.doi.org/10.1007/978-1-4684-4730-9_5 ]
Tu S C, Hsu H and Tai W K. 2010a. Permutation steganography for polygonal meshes based on coding tree. International Journal of Virtual Reality, 9(4): 55-60 [DOI: 10.20870/ijvr.2010.9.4.2790]
Tu S C and Tai W K. 2012. A high-capacity data-hiding approach for polygonal meshes using maximum expected level tree. Computers and Graphics, 36(6): 767-775 [DOI: 10.1016/j.cag.2012.06.002]
Tu S C, Tai W K, Isenburg M and Chang C C. 2010b. An improved data hiding approach for polygon meshes. The Visual Computer, 26(9): 1177-1181 [DOI: 10.1007/s00371-009-0398-1]
Wang C M and Cheng Y M. 2005. An efficient information hiding algorithm for polygon models. Computer Graphics Forum, 24(3): 591-600 [DOI: 10.1111/j.1467-8659.2005.00884.x]
Wang C M and Wang P C. 2006. Steganography on point-sampled geometry. Computers and Graphics, 30(2): 244-254 [DOI: 10.1016/j.cag.2006.01.030]
Wang Y M, Kong L S, Qian Z X, Feng G R, Zhang X P and Zheng J M. 2019. Breaking permutation-based mesh steganography and security improvement. IEEE Access, 7: 183300-183310 [DOI: 10.1109/access.2019.2960455]
Yang Y and Ivrissimtzis I. 2014. Mesh discriminative features for 3D steganalysis. ACM Transactions onMultimedia Computing, Communications, and Applications, 10(3): #27 [DOI: 10.1145/2535555]
Yang Y, Peyerimhoff N and Ivrissimtzis I. 2013. Linear correlations between spatial and normal noise in triangle meshes. IEEE Transactions on Visualization and Computer Graphics, 19(1): 45-55 [DOI: 10.1109/tvcg.2012.106]
Zhang W M, Zhang Z, Zhang L L, Li H Y and Yu N H. 2017. Decomposing joint distortion for adaptive steganography. IEEE Transactions on Circuits and Systems for Video Technology, 27(10): 2274-2280 [DOI: 10.1109/tcsvt.2016.2587388]
Zhou H, Chen K J, Zhang W M, Qin C and Yu N H. 2021a. Feature-preserving tensor voting model for mesh steganalysis. IEEE Transactions on Visualization and Computer Graphics, 27(1): 57-67 [DOI: 10.1109/tvcg.2019.2929041]
Zhou H, Chen K J, Zhang W M, Yao Y Z and Yu N H. 2019. Distortion design for secure adaptive 3-D mesh steganography. IEEE Transactions on Multimedia, 21(6): 1384-1398 [DOI: 10.1109/tmm.2018.2882088]
Zhou H, Zhang W M, Chen K J, Li W X and Yu N H. 2021b. Three-dimensional mesh steganography and steganalysis: a review. IEEE Transactions on Visualization and Computer Graphics: #3075136 [DOI: 10.1109/TVCG.2021.3075136]
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