Ma, YuanYuan, Luo, Xiangyang, Li, Zhenyu et al. (2 more authors) (2019) Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion. In: Proc. ACM Turing Celebration Conference - China. ACM
Abstract
In order to improve the detection of hidden information in signals, additional features are considered as inputs for steganalysers. This research study proposes a feature selection method based on Weighted Inner-Inter class Distance and Dispersion (W2ID) criterion in order to reduce the steganalytic feature dimensionality. The definition of W2ID criterion and an algorithm determining the weight for the W2ID criterion based on the frequency statistical weighting method are proposed. Then, the W2ID criterion is applied in the decision rough set α-positive domain reduction, producing the W2ID-based feature selection method. Experimental results show that the proposed method can reduce the dimension of the feature space and memory requirements of Gabor Filter Residuals (GFR) feature while maintaining or improving the detection accuracy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: | 27 Nov 2020 10:20 |
Last Modified: | 16 Oct 2024 11:05 |
Published Version: | https://doi.org/10.1145/3321408.3321572 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3321408.3321572 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168468 |