基于贝叶斯分类器的图像隐写分析

张兴春; 孙寿健

doi:10.3788/YJYXS20173207.0560

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基于贝叶斯分类器的图像隐写分析

材料科学 | 更新时间：2020-08-12

- 基于贝叶斯分类器的图像隐写分析
- Image steganalysis based on bayesian classifier
- 液晶与显示 2017年32卷第7期页码：560-566
- 作者机构：
  
  1.武警黑龙江总队司令部, 黑龙江哈尔滨 150028
  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：
  
  [ "张兴春(1980－),男,黑龙江哈尔滨人,主要从事信息安全、隐写分析以及图像处理的工作" ]
  [ "孙寿健(1990－),男,山东临邑人,硕士,主要从事信息隐藏以及信号处理等工作. E-mail：zynwj08@163.com" ]
- 基金信息：
  
  国家自然科学基金(No.61403417)
- DOI：10.3788/YJYXS20173207.0560
  中图分类号： TP391
- 收稿日期：2017-01-06，
  
  录用日期：2017-03-23，
  
  纸质出版日期：2017-07-05
- 稿件说明：
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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.

Xing-chun ZHANG, Shou-jian SUN. Image steganalysis based on bayesian classifier[J]. Chinese journal of liquid crystals and displays, 2017, 32(7): 560-566.
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. DOI： 10.3788/YJYXS20173207.0560.

Xing-chun ZHANG, Shou-jian SUN. Image steganalysis based on bayesian classifier[J]. Chinese journal of liquid crystals and displays, 2017, 32(7): 560-566. DOI： 10.3788/YJYXS20173207.0560.

摘要

集成分类器是目前用于图像隐写分析的主流分类器。为提高集成分类器的检测精度，针对集成分类器基分类器组合方法过于简单，无法体现基分类器之间的内在联系，不能从整体上对结果进行判定的缺点，依据图像特征在集成分类器分类超平面上的投影值服从多维正态分布这一特性，提出了一种基于贝叶斯分类器的图像隐写分析算法。首先基于随机森林算法生成若干基分类器，然后计算类条件概率密度函数与先验概率并训练贝叶斯分类器，最后使用经过训练的贝叶斯分类器代替简单投票方法进行分类判决。算法的检测错误率比以往算法平均降低了1.6%，ROC曲线比简单投票方法更接近于左上角，即具有更高的检测率，AUC值平均增长约2.12%，并且训练时间仅有少量提高，最大提高约2.610 s。可以有效提高集成分类器的检测精度。

Abstract

Recently

ensemble classifier is predominantly used for steganalysis of digital media. For increasing the detection accuracy of ensemble classifier and focused on the defects that the fusion method of ensemble classifier is too simple to reflect the correlation of base learners and can't give an overall result

an algorithm based on Bayesian Classifier is proposed according to a characteristic that the projection of feature on the hyper planes of ensemble classifier obeys the framework of the multivariate Gaussian distribution. At first

some base learners were generated based on the random forest

then the probability density function and prior probability were calculated for training a Bayesian classifier. At last

the algorithm uses a Bayesian Classifier instead of the majority voting rule to fuse the decisions of base learners. The error detection rate lower than before by an average of 1.6%

ROC curve is closer to the top left corner than simple voting method

which has higher detection rate

the growth of AUC value is about 2.12%

and the training time increase about 2.610 s. The proposed method is able to increase steganalysis performance of ensemble classifier.

关键词

Keywords

references

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