Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion

YuanYuan Ma; Xiangyang Luo; Zhenyu Li; Yi Zhang; Adrian Gheorghe Bors

doi:10.1145/3321408.3321572

Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion

YuanYuan Ma, Xiangyang Luo, Zhenyu Li, Yi Zhang, Adrian Gheorghe Bors

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proc. ACM Turing Celebration Conference - China
Publisher	ACM
Number of pages	5
ISBN (Print)	978-1-4503-7158-2
DOIs	https://doi.org/10.1145/3321408.3321572
Publication status	Published - May 2019

Access to Document

10.1145/3321408.3321572

ACMTURC2019

Cite this

@inproceedings{53c88cee46b0441e8536ef0b1c8856da,

title = "Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion",

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.",

author = "YuanYuan Ma and Xiangyang Luo and Zhenyu Li and Yi Zhang and Bors, {Adrian Gheorghe}",

year = "2019",

month = may,

doi = "10.1145/3321408.3321572",

language = "English",

isbn = "978-1-4503-7158-2",

booktitle = "Proc. ACM Turing Celebration Conference - China",

publisher = "ACM",

address = "United States",

}

TY - GEN

T1 - Feature selection method for image steganalysis based on weighted inner-inter class distance and dispersion criterion

AU - Ma, YuanYuan

AU - Luo, Xiangyang

AU - Li, Zhenyu

AU - Zhang, Yi

AU - Bors, Adrian Gheorghe

PY - 2019/5

Y1 - 2019/5

N2 - 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.

AB - 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.

U2 - 10.1145/3321408.3321572

DO - 10.1145/3321408.3321572

M3 - Conference contribution

SN - 978-1-4503-7158-2

BT - Proc. ACM Turing Celebration Conference - China

PB - ACM

ER -