Rethinking the high capacity 3D steganography: Increasing its resistance to steganalysis

Li, Zhenyu; Beugnon, Sebastien; Puech, William; Borş, Adrian G.

doi:10.1109/icip.2017.8296333

Cited by 17 publications

(18 citation statements)

References 20 publications

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“…In addition, the image is just one of the types of cover which may be maliciously used, while text, voice, protocol packets, and other types of data may also be utilized to transit secret confidential information. [32][33][34] Therefore, how to reliably detect the hidden secret information of various multimedia data transmitted in the Internet of Things should be solved for ensuring the security of the Internet of Things.…”

Section: Resultsmentioning

confidence: 99%

Color image steganalysis based on channel gradient correlation

Kang

Liu

Yang

et al. 2019

International Journal of Distributed Sensor Networks

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It is one of the potential threats to the Internet of Things to reveal confidential messages by color image steganography. The existing color image steganalysis algorithm based on channel geometric transformation measures owns higher accuracy than the others, but it fails to utilize the correlation between the gradient amplitudes of different color channels. Therefore, this article points out that the color image steganography weakens the correlation between the gradient amplitudes of different color channels and proposes a color image steganalysis algorithm based on channel gradient correlation. The proposed algorithm extracts the co-occurrence matrix feature from the gradient amplitude residuals among different color channels and then combines it with the existing color image steganalysis features to train the ensemble classifier for color image steganalysis. The experimental results show that, for WOW and S-UNIWARD steganography, compared with the existing algorithms, the proposed algorithm outperforms the existing algorithms.

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Section: Resultsmentioning

confidence: 99%

Color image steganalysis based on channel gradient correlation

Kang

Liu

Yang

et al. 2019

International Journal of Distributed Sensor Networks

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“…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%

“…Recently, 3D mesh steganography technologies have been actively investigated due to the rapid expansion of 3D techniques, and they can be mainly classified into two categories: low-capacity [2,3,4,5,6] and high-capacity steganography [7,7,8,9,10,11,12]. Correspondingly, to detect whether a mesh contains hidden data, 3D mesh steganalysis algorithms [13,14,15,16,17] are being developed.…”

Section: Introductionmentioning

confidence: 99%

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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“…3D objects are becoming increasingly used in many application areas, such as computer aided design, 3D printing, virtual reality, augmented reality, medical imaging and so on. A number of watermarking and steganographic methods have been proposed for embedding information into the 3D objects for various applications [1,2,3,4,5,6]. The embedding changes produced to the 3D objects are supposed not to be noticeable by the naked eye.…”

Section: Introductionmentioning

confidence: 99%

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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Rethinking the high capacity 3D steganography: Increasing its resistance to steganalysis

Cited by 17 publications

References 20 publications

Color image steganalysis based on channel gradient correlation

Color image steganalysis based on channel gradient correlation

Feature-Preserving Tensor Voting Model for Mesh Steganalysis

3D Steganalysis Using the Extended Local Feature Set

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