基于图像来源分类的最小化虚警隐写分析模型

杨培韬; 张卫明; 俞能海

doi:10.11959/j.issn.1000-436x.2016282

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基于图像来源分类的最小化虚警隐写分析模型

学术通信 | 更新时间：2024-06-05

- 基于图像来源分类的最小化虚警隐写分析模型
- Reducing false positives of steganalysis via classification of image-acquiring sources
- 通信学报 2016年37卷第12期页码：165-170
- 作者机构：
  
  中国科学技术大学中科院电磁空间信息重点实验室，安徽合肥 230001
- 作者简介：
  
  [ "杨培韬（1991-），男，安徽安庆人，中国科学技术大学硕士生，主要研究方向为隐写分析。" ]
  [ "张卫明（1976-），男，河北保定人，博士，中国科学技术大学副教授，主要研究方向为信息隐藏、密码学。" ]
  [ "俞能海（1964-），男，安徽无为人，博士，中国科学技术大学教授，主要研究方向为视频处理与多媒体通信、无线通信中的信号处理与分析、信息隐藏与信息安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61502007);国家自然科学基金资助项目(U1636201);中国博士后科学基金资助项目(2015M582015);中科院先导科技专项基金资助项目(XDA06030601)
- DOI：10.11959/j.issn.1000-436x.2016282
  中图分类号： TN309
- 网络出版日期：2016-12，
  
  纸质出版日期：2016-12-25
- 稿件说明：
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杨培韬, 张卫明, 俞能海. 基于图像来源分类的最小化虚警隐写分析模型[J]. 通信学报, 2016,37(12):165-170.

Pei-tao YANG, Wei-ming ZHANG, Neng-hai YU. Reducing false positives of steganalysis via classification of image-acquiring sources[J]. Journal on communications, 2016, 37(12): 165-170.
杨培韬, 张卫明, 俞能海. 基于图像来源分类的最小化虚警隐写分析模型[J]. 通信学报, 2016,37(12):165-170. DOI： 10.11959/j.issn.1000-436x.2016282.

Pei-tao YANG, Wei-ming ZHANG, Neng-hai YU. Reducing false positives of steganalysis via classification of image-acquiring sources[J]. Journal on communications, 2016, 37(12): 165-170. DOI： 10.11959/j.issn.1000-436x.2016282.

摘要

在实真场景中，在载体失配（CSM

cover source mismatch）条件下降低虚警率是隐写分析的一个巨大挑战，提出了一种新的模型来处理该问题。该方法由来源分类器首先判断图像的来源，继而利用相关来源图像训练而成的隐写分类器判断待测图像是否为载密。在这个过程中，通过对模型参数的调节减小虚警率。实验结果表明，这种方法可以在较大准确率的前提下最小化虚警率。

Abstract

In the real world

reducing false positive rates in the case of cover source mismatch (CSM) was a big challenge for steganalysis. A novel model was proposed to solve the problem. The proposed method determines the im-age-acquiring source firstly by a source detector and then detecting the steg images in each source with a steganalyzer trained for this source. The false positive rate was reduced by solving a parameter model. The experimental results show that this novel method can reach lower false positive rates for larger true positive rates.

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Keywords

references

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