Spatial-domain steganalytic feature selection based on three-way interaction information and KS test

  • Xiangyuan Gu
  • Jichang GuoEmail author
  • Huiwen Wei
  • Yanhong He
Methodologies and Application


To select informative features from steganalytic features, a spatial-domain steganalytic feature selection method based on three-way interaction information and Kolmogorov–Smirnov (KS) test is proposed. Three-way interaction information is employed to rank all the features, and KS test is exploited to remove redundant features. Feature selection process of the proposed method is presented as follows: It calculates mutual information between features and the class label and selects the feature with the maximum value. Then, it loops to calculate three-way interaction information among each candidate feature, the previously selected feature and the class label and select the candidate feature with the maximum value. Following that, it calculates KS test between features and compares an obtained parameter with the predefined significance level for eliminating redundant features. To validate the performance of the proposed method, several typical feature ranking methods based on information measure and spatial-domain steganalytic feature selection methods are adopted for performance comparisons. Experimental results demonstrate that the proposed method can achieve better feature selection performance.


Spatial-domain steganalytic features Three-way interaction information Feature selection Steganalysis KS test 



The authors would like to thank Jicang Lu and his co-authors and Morteza Darvish Morshedi Hosseini and his co-author for providing their codes.

Compliance with ethical standards


This study was funded by the National Natural Science Foundation of China (61771334).

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiangyuan Gu
    • 1
  • Jichang Guo
    • 1
    Email author
  • Huiwen Wei
    • 1
  • Yanhong He
    • 1
  1. 1.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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