Li, Zhenyu and Bors, Adrian Gheorghe orcid.org/0000-0001-7838-0021 (2018) Selection of Robust and Relevant Features for 3-D Steganalysis. IEEE Transactions on Cybernetics. pp. 1989-2001. ISSN 2168-2267
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 modelling 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 steganalyser 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: Pearson Correlation Coefficient (PCC) and the Mutual Information Criterion (MIC).
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. |
Keywords: | 3-D steganalysis,Data mining,Feature extraction,Machine learning,Machine learning algorithms,Robustness,Shape,Training,cover source mismatch,feature selection |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 05 Jun 2019 12:30 |
Last Modified: | 18 Dec 2024 00:13 |
Published Version: | https://doi.org/10.1109/TCYB.2018.2883082 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TCYB.2018.2883082 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146982 |