通信学报 ›› 2016, Vol. 37 ›› Issue (12): 165-170.doi: 10.11959/j.issn.1000-436x.2016282

• 学术通信 • 上一篇    下一篇

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   

  1. 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
  • 出版日期:2016-12-25 发布日期:2017-05-15
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目;中国博士后科学基金资助项目;中国科学院战略性先导专项基金资助项目

Reducing false positives of steganalysis via classification of image-acquiring sources

Pei-tao YANG,Wei-ming ZHANG(),Neng-hai YU   

  1. CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei 230001, China
  • Online:2016-12-25 Published:2017-05-15
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China;The China Postdoctoral Science Foundation;The Strategic Priority Research Program of the Chinese Academy of Sciences

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。

关键词: 虚警率, 失配, 隐写分析, 最小化虚警模型

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.

Key words: false positive, mismatch, steganalysis, minimum false positive model

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