Abstract
While 3-D steganography and digital watermarking represent methods for embedding information into 3-D objects, 3-D steganalysis aims to find the hidden information. Previous research studies have shown that by estimating the parameters modeling the statistics of 3-D features and feeding them into a classifier we can identify whether a 3-D object carries secret information. For training the steganalyzer, such features are extracted from cover and stego pairs, representing the original 3-D objects and those carrying hidden information. However, in practical applications, the steganalyzer would have to distinguish stego-objects from cover-objects, which most likely have not been used during the training. This represents a significant challenge for existing steganalyzers, raising a challenge known as the cover source mismatch (CSM) problem, which is due to the significant limitation of their generalization ability. This paper proposes a novel feature selection algorithm taking into account both feature robustness and relevance in order to mitigate the CSM problem in 3-D steganalysis. In the context of the proposed methodology, new shapes are generated by distorting those used in the training. Then a subset of features is selected from a larger given set, by assessing their effectiveness in separating cover-objects from stego-objects among the generated sets of objects. Two different measures are used for selecting the appropriate features: 1) the Pearson correlation coefficient and 2) the mutual information criterion.
Highlights
Data hiding has many applications, including intellectual protection, marketing, storing information for contextual use and so on
In the following we provide the assessment of the proposed methodology for addressing the cover source mismatch in 3D steganalysis
This research study proposes a solution for the cover source mismatch problem in the context of 3-D steganalysis
Summary
Data hiding has many applications, including intellectual protection, marketing, storing information for contextual use and so on. The Cover Source Mismatch (CSM) problem in 3-D steganalysis addresses the robustness of steganalyzers to be trained using a set of cover and stego 3-D objects characterized by certain properties and being able to identify the stegoobjects when tested on a set of objects with different surface properties. The proposed methodology, whilst removing some of the redundant features, considers only those features that enable an appropriate generalization from the training set to the wider space of stego and cover-objects During this stage we increase the diversity of the objects by considering local perturbations on the surface of objects. Such perturbations consists of mesh simplifications and noise additions and these would result in changes of the geometrical characteristics of the cover sources, generating objects which are quite different from the original ones when considering the local surface properties These are considered as cover-objects and used for hiding information through steganography resulting in sets of stego-objects. The Quadratic Discriminant [37] and the FLD ensemble [16] have been used as machine learning methods for discriminating the cover-objects from stego-objects
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