Steganalysis of 3D objects using statistics of local feature sets

Li, Zhenyu; Borş, Adrian G.

doi:10.1016/j.ins.2017.06.011

Cited by 22 publications

(29 citation statements)

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“…In this section, we provide a brief introduction of the 3D steganalytic approach based on the local feature set, LFS76, proposed in [18]. The 3D steganalyzer is trained through the following processing stages: preprocessing, feature extraction and supervised learning.…”

Section: Local Feature Set For 3d Steganalysismentioning

confidence: 99%

“…19 geometric features, characterizing the local geometry of 3D shapes, are extracted from the original mesh, O, and its smoothed version, O ′ in order to be used as inputs to the steganalyzer in [18]. These geometric features define the vertex coordinates and norms in the Cartesian and Laplacian coordinate systems, the face normal, the dihedral angle, the vertex normal, the Gaussian curvature, the curvature ratio, the vertex coordinates and edge length in the spherical coordinate system.…”

Section: Local Feature Set For 3d Steganalysismentioning

confidence: 99%

“…In the following we consider a set of new 3D features, in order to be added to those that have been already used in [15,18], in order to improve the performance of 3D steganalyzers. We propose to use the edge vectors in both the Cartesian and the Laplacian coordinate systems to extract steganalytic features.…”

Section: Extended Local Feature Setmentioning

confidence: 99%

“…Nevertheless, the steganalysis results for 3D waveletbased watermarking algorithms require further improvement, according to the experimental results reported in [18]. In this paper, we propose to use some new features derived from the edge vectors of the mesh objects for 3D steganalysis.…”

Section: Introductionmentioning

confidence: 99%

“…Li and Bors proposed the 52-dimensional Local Feature Set (LFS52) in [17], which simplified the 208-dimensional feature set YANG208 proposed in [15] and included some new geometric feature for steganalysis, such as the vertex normal, the curvature ratio and the Gaussian curvature. In addition to LFS52, other geometric features extracted from the mesh, represented in the spherical coordinate system, form the 76-dimensional feature set for 3D steganalysis in [18]. Meanwhile, Kim et al [19] extended the approach from [17], and proposed to use some additional features such as the edge normal, the mean curvature and the total curvature in addition to LFS52, forming the LFS64 feature set for 3D steganalysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

3D Steganalysis Using the Extended Local Feature Set

Gong²,

Liu³

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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3D steganalysis aims to find the changes embedded through steganographic or information hiding algorithms into 3D models. This research study proposes to use new 3D features, such as the edge vectors, represented in both Cartesian and Laplacian coordinate systems, together with other steganalytic features, for improving the results of 3D steganalysers. In this way the local feature vector used by the steganalyzer is extended to 124 dimensions. We test the performance of the extended local feature set, and compare it to four other steganalytic features, when detecting the stego-objects watermarked by six information hiding algorithms.

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Section: Local Feature Set For 3d Steganalysismentioning

confidence: 99%

Section: Local Feature Set For 3d Steganalysismentioning

confidence: 99%

Section: Extended Local Feature Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

3D Steganalysis Using the Extended Local Feature Set

Gong²,

Liu³

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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3D Steganalysis Using Laplacian Smoothing at Various Levels

Liu

Borş

2018

Cloud Computing and Security

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3D objects are becoming ubiquitous while being used by many mobile and social network applications. Meanwhile, such objects are also becoming a channel being used for covert communication. Steganalysis aims to identify when information is transferred in such ways. This research study analyses the influence of the 3D object smoothing, which is an essential step before extracting the features used for 3D steganalysis. During the experimental results, the efficiency when employing various degrees of 3D smoothing, is assessed in the context of steganalysis.

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