Image steganalysis algorithm based on feature ranking

Xing-chun ZHANG; Shou-jian SUN

doi:10.3788/YJYXS20183306.0490

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Image steganalysis algorithm based on feature ranking

Image Processing | 更新时间：2020-07-06

- Image steganalysis algorithm based on feature ranking
- Chinese Journal of Liquid Crystals and Displays Vol. 33, Issue 6, Pages: 490-496(2018)
- 作者机构：
  
  1.武警黑龙江总队司令部, 黑龙江哈尔滨 150000
  2.武警黑龙江总队佳木斯支队司令部, 黑龙江佳木斯 154000
  3.网络与信息安全武警部队重点实验室, 陕西西安 710086
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20183306.0490
  CLC： TP391
- Received：12 January 2018，
  
  Accepted：23 March 2018，
  
  Published：2018-06
- 稿件说明：
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Xing-chun ZHANG, Shou-jian SUN. Image steganalysis algorithm based on feature ranking[J]. Chinese journal of liquid crystals and displays, 2018, 33(6): 490-496.
DOI：

Xing-chun ZHANG, Shou-jian SUN. Image steganalysis algorithm based on feature ranking[J]. Chinese journal of liquid crystals and displays, 2018, 33(6): 490-496. DOI： 10.3788/YJYXS20183306.0490.

摘要

为提高用于隐写分析的集成分类器的检测精度，提出一种基于特征排名的隐写分析算法。首先计算每维检测特征的互信息得分并根据得分高低将特征进行排名，然后设置分界点将特征分为重要特征区域与普通特征区域，依据设定的抽样比例从两个区域随机抽取特征组成不同的特征子空间并训练集成分类器。最后使用集成分类器进行分类。实验结果表明，针对使用nsF5及S-UNIWARD算法进行隐写的频域及空域图像，本算法较传统分类器在检测错误率方面分别平均下降约0.006 5和0.006 2，具有较好的检测效果。针对频域与空域中两种不同的隐写算法，与传统的集成分类器相比，该算法具有更高的检测精度。

Abstract

In order to enhance the detection rate of ensemble classifier

an algorithm based on feature ranking is proposed. First

the original feature is sorted according mutual information score. Then the sorted feature is divided into important feature part and common feature part according to the divide point. The feature subset is formed by selecting features randomly in each part according to the given sampling rate. Experimental results show that

our method detects two format images after embedding nsF5 and S-UNIWARD

the false detection is lower than classical ensemble classifier 0.0065 to.jpeg images

and the false detection is lower than 0.0062 to.bmp images. Compared with typical ensemble classifier

the proposed method is more effective than the different stego algorithms in frequency domain and spatial domain.

关键词

Keywords

references

SEDIGHI V, FRIDRICH J. Effect of saturated pixels on security of steganographic schemes for digital images[C]// Proceedings of 2016 IEEE International Conference on Image Processing . Phoenix, AZ, USA: IEEE, 2016: 2747-2751.

TANG W X, LI H D, LUO W Q, et al . Adaptive steganalysis based on embedding probabilities of pixels[J]. IEEE Transactions on Information Forensics and Security , 2016, 11(4):734-745.

PEVNÝ T, FILLER T, BAS P. Using High-dimensional Image Models to Perform Highly Undetectable Steganography [M]//BÖHME R, FONG P W L, SAFAVI-NAINI R. Information Hiding . Berlin, Heidelberg: Springer, 2010: 161-177.

DENEMARK T, FRIDRICH J, HOLUB V. Further study on the security of S-UNIWARD[C]// Proceedings of SPIE Volume 9028, Media Watermarking , Security , and Forensics 2014. San Francisco, California, USA: SPIE, 2014: 902805.

FILLER T, JUDAS J, FRIDRICH J. Minimizing additive distortion in steganography using syndrome-trellis codes[J]. IEEE Transactions on Information Forensics and Security , 2011, 6(3):920-935.

FRIDRICH J, FILLER T. Practical methods for minimizing embedding impact in steganography[C]// Proceedings of SPIE Volume 6505, Security , Steganography , and Watermarking of Multimedia Contents IX . San Francisco, California, United States: SOIE, 2007: 650502.

张健, 于晓洋, 黄海霞, 等.可抵抗SPA分析的HSBH改进算法[J].光学精密工程, 2008, 16(7):1295-1302.

ZHANG J, YU X Y, HUANG H X, et al . Improved HSBH algorithm against SPA[J]. Optics and Precision Engineering , 2008, 16(7):1295-1302. (in Chinese)

KODOVSKÝ J, FRIDRICH J J. Calibration revisited[C]// Proceedings of 11 th ACM Workshop on Multimedia and Security . Princeton, NJ, USA: ACM, 2009: 63-74.

FRIDRICH J, KODOVSKY J. Rich models for steganalysis of digital images[J]. IEEE Transactions on Information Forensics and Security , 2012, 7(3):868-882.

赵宝琴, 袁志民.面向隐写分析的图像富模型特征的改进[J].科学技术与工程, 2016, 16(31):56-60, 65.

ZHAO B Q, YUAN Z M. Steganalysis oriented improved rich image model features[J]. Science Technology and Engineering , 2016, 16(31):56-60, 65. (in Chinese)

KODOVSKY J, FRIDRICH J, HOLUB V. Ensemble classifiers for steganalysis of digital media[J]. IEEE Transactions on Information Forensics and Security , 2012, 7(2):432-444.

张兴春, 孙寿健.基于贝叶斯分类器的图像隐写分析[J].液晶与显示, 2017, 32(7):560-566.

ZHANG X C, SUN S J. Image steganalysis based on Bayesian classifier[J]. Chinese Journal of Liquid Crystals and Displays , 2017, 32(7):560-566.

王秀霞. 分类器的选择性集成及其差异性研究[D]. 兰州: 兰州理工大学, 2011: 15-18.

WANGX X. Research on classifier selective ensemble method and thire diversity measurement[D]. Lanzhou: Lanzhou University of Technology, 2011: 15-18. (in Chinese)

LIU Q, SHI S H, ZHU H M, et al . A mutual information-based hybrid feature selection method for software cost estimation using feature clustering[C]// Proceedings of the 38 th Annual Computer Software and Applications Conference . Västerås, Sweden: IEEE, 2014: 27-32.

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Related Institution

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