Feature Selection Method For Image Steganalysis Based on Weighted Inner-Inter Class Distance and Dispersion Criterion

摘要：

　　In order to improve the detection of hidden information in sig-nals,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)cri-terion 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 statis-tical 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 Resid-uals(GFR)feature while maintaining or improving the detection accuracy.

关键词： Steganalysis Feature selection W2ID criterion Reduction Steganalysis-α method

作者: Yuanyuan Ma Xiangyang Luo Zhenyu Li Yi Zhang Adrian G.Bors

作者单位: State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou,China Department of Computer Science,University of York York,UK

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 631-635

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）