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- W2904653265 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." @default.
- W2904653265 created "2018-12-22" @default.
- W2904653265 creator A5002932489 @default.
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- W2904653265 date "2020-05-01" @default.
- W2904653265 modified "2023-09-24" @default.
- W2904653265 title "Selection of Robust and Relevant Features for 3-D Steganalysis" @default.
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- W2904653265 doi "https://doi.org/10.1109/tcyb.2018.2883082" @default.
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