Selection of Robust and Relevant Features for 3-D Steganalysis.

Clicks: 159
ID: 17361
2018
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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.
Reference Key
li2018selectionieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li, Zhenyu;Bors, Adrian G;
Journal ieee transactions on cybernetics
Year 2018
DOI
10.1109/TCYB.2018.2883082
URL
Keywords Keywords not found

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