Distortion‐Free Steganography for Polygonal Meshes

Bogomjakov, Alexander; Gotsman, Craig; Isenburg, Martin

doi:10.1111/j.1467-8659.2008.01161.x

Cited by 50 publications

(61 citation statements)

References 20 publications

(13 reference statements)

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“…The embedding scheme hides secret messages in the vertices of 3D polygon models. Similarly, Bogomjakov et al [78], hide a message in the indexed representation of a mesh by permuting the order in which faces and vertices are stored. Although, such methods claim higher embedding capacity, however time complexity to generate the mesh and then rendering can be an issue.…”

Section: Adaptive Steganographymentioning

confidence: 99%

Digital image steganography: Survey and analysis of current methods

et al. 2010

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Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganography's ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the object-oriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed.

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Section: Adaptive Steganographymentioning

confidence: 99%

Digital image steganography: Survey and analysis of current methods

et al. 2010

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“…[Wang and Cheng 2005] increase the embedding capacity and reduce the distortion by employing a multi-level embedding procedure. Bogomjakov et al [Bogomjakov et al 2008] propose a distortion-free steganographic method, using the redundancy in the indexed representation of a mesh by permuting the order in which faces and vertices are stored. Tu et al [Tu et al 2010] improves the efficiency in the original mapping of [Bogomjakov et al 2008], further increasing its capacity.…”

Section: D Watermarkingmentioning

confidence: 99%

“…Bogomjakov et al [Bogomjakov et al 2008] propose a distortion-free steganographic method, using the redundancy in the indexed representation of a mesh by permuting the order in which faces and vertices are stored. Tu et al [Tu et al 2010] improves the efficiency in the original mapping of [Bogomjakov et al 2008], further increasing its capacity. Chao et al [Chao et al 2009] propose a high-capacity, low-distortion method based on vertex projection onto the principal axis.…”

Section: D Watermarkingmentioning

confidence: 99%

Mesh Discriminative Features for 3D Steganalysis

Yang

Ivrissimtzis

2014

ACM Trans. Multimedia Comput. Commun. Appl.

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. We propose a steganalytic algorithm for triangle meshes, based on the supervised training of a classifier by discriminative feature vectors. After a normalization step, the triangle mesh is calibrated by one step of Laplacian smoothing and then a feature vector is computed, encoding geometric information corresponding to vertices, edges and faces. For a given steganographic or watermarking algorithm, we create a training set containing unmarked meshes and meshes marked by that algorithm, and train a classifier using Quadratic Discriminant Analysis. The performance of the proposed method was evaluated on six well-known watermarking/steganographic schemes with satisfactory accuracy rates.

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“…Another limitation is that this approach cannot withstand certain malicious attacks such as smoothing, additional noise, non uniform scaling, simplification, and vertices resampling [19]. As a result, the proposed approach is not suitable for the applications of digital content protection and authentication .Similarly, Bogomjakov et al [55], hide a message in the indexed representation of a mesh by permuting the order in which faces and vertices are stored. The permutation is relative to a reference ordering that encoder and decoder derive from the mesh connectivity in a consistent manner.…”

Section: Milestones For Adaptive Steganographymentioning

confidence: 99%