A high-capacity data-hiding approach for polygonal meshes using maximum expected level tree

Tu, Shih-Chun; Tai, Wen‐Kai

doi:10.1016/j.cag.2012.06.002

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…Three steganographic schemes, namely, Chao [7], VND [12], and Li [10], are employed for evaluation. Note that we do not consider distortionless schemes [18,19,20], though these steganalytic features cannot discriminate these stegos generated by order permutation based steganography from covers, they can be easily detected by specifically designed detector. Because the orders of faces are generally highly relevant for normal meshes, when the stego meshes are created by distortionless methods, the orders of faces are uncorrelated.…”

Section: Comparison With the State-of-the-art Steganalytic Methodsmentioning

confidence: 99%

“…High-capacity based 3D mesh steganography can be classified into two categories: distortionless steganography based on order permutation [18,19,20], and distorted steganography based on vertex shifting [7,8,9,10,11,12]. For distortionless steganography, Bogomjakov et al [18] propose to hide messages in the indexed representation of a mesh by permuting the order in which faces and vertices are stored.…”

Section: D Mesh Steganographymentioning

confidence: 99%

“…Tan et al [19] consider the vertex index as a message block, and propose three embedding strategies: vertex index embedding, dynamic-length bit-string mapping, and repeated bits embedding to enlarge embedding capacity. Tu and Tai [20] propose to use maximum expected level tree based on a message probability model to embed data, which owns larger capacity than previous methods. For distorted steganography, Chao et al [7] provide multilayered highcapacity reversible steganography with space modulation and demodulation techniques on the principal axes by vertex projection.…”

Section: D Mesh Steganographymentioning

confidence: 99%

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Feature-Preserving Tensor Voting Model for Mesh Steganalysis

Zhou

Chen

Zhang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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The standard tensor voting technique shows its versatility in tasks such as object recognition and semantic segmentation by recognizing feature points and sharp edges that can segment a model into several patches. We propose a neighborhood-level representation-guided tensor voting model for 3D mesh steganalysis. Because existing steganalytic methods do not analyze correlations among neighborhood faces, they are not very effective at discriminating stego meshes from cover meshes. In this paper, we propose to utilize a tensor voting model to reveal the artifacts caused by embedding data. In the proposed steganalytic scheme, the normal voting tensor (NVT) operation is performed on original mesh faces and smoothed mesh faces separately. Then, the absolute values of the differences between the eigenvalues of the two tensors (from the original face and the smoothed face) are regarded as features that capture intricate relationships among the vertices. Subsequently, the extracted features are processed with a nonlinear mapping to boost the feature effectiveness. The experimental results show that the proposed feature sets prevail over state-of-the-art feature sets including LFS64 and ELFS124 under various steganographic schemes.

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Section: Comparison With the State-of-the-art Steganalytic Methodsmentioning

confidence: 99%

Section: D Mesh Steganographymentioning

confidence: 99%

Section: D Mesh Steganographymentioning

confidence: 99%

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Feature-Preserving Tensor Voting Model for Mesh Steganalysis

Zhou

Chen

Zhang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…Due to the simplification in computation, the approach is highly efficient, with only a loss of one bit per vertex for capacity than the optimal case. To further approach the optimal embedding capacity, improved works were presented in [8], [9], [23], where techniques such as building a special type of binary tree were adopted to find the next element in π.…”

Section: ) Permutation-based Steganographymentioning

confidence: 99%

“…Polygonal meshes, especially triangular meshes, are the most commonly used types of 3D geometry. In general, mesh steganography approaches fall into two major categories, the geometry-based approaches [3]- [5] and the permutationbased approaches [6]- [9]. The geometry-based approaches embed secret data into meshes by slightly perturbing the position of the vertices.…”

Section: Introductionmentioning

confidence: 99%

Breaking Permutation-Based Mesh Steganography and Security Improvement

et al. 2019

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Permutation-based steganography in polygonal meshes can provide considerably large embedding capacities for hiding secret messages. However, corresponding steganalysis techniques against such methods have never been studied. This paper identifies the essential differences between naturally generated meshes and meshes produced by permutation-based steganography methods. It is found that the two types of meshes differ significantly in the distribution of topological distances between consecutive mesh elements. Therefore, by measuring the orderliness of the vertex list and the face list of meshes, we develop solutions for several mesh steganalysis problems. These solutions are effective, leading to high detection accuracy; and they are also universal, requiring no knowledge such as which steganography method is used and what data embedding rate is adopted for the detection mechanism to work. Moreover, this paper also presents a security-improved permutation-based mesh steganography approach, by taking advantage of the connectivity information of polygonal meshes and establishing a good trade-off between embedding capacity and undetectability. Without bringing global changes, our approach embeds secret messages into local neighborhoods on meshes. As a result, meshes generated by the proposed steganography approach tend to have natural structures that are unlikely to draw suspicions to steganalyzers.

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