文章摘要
引用本文:何凤英,钟尚平,徐巧芬.基于特征融合和改进RSM集成分类的BMP隐写检测[J].福州大学学报(自然科学版),2014,42(5):661~665
基于特征融合和改进RSM集成分类的BMP隐写检测
BMP steganalysis based on feature fusion and improved RSM ensemble
  
DOI:10.7631/issn.1000-2243.2014.05.0661
中文关键词: 隐写检测  丰富的高维模型(high-dimensional rich model,HDRM)  集成分类  序列前向选择  特征融合
英文关键词: steganalysis  random subspace method  integrated classification  sequential forward selection  feature fusion
基金项目:
作者单位
何凤英 福州大学数学与计算机科学学院福建 福州 350116 
钟尚平  
徐巧芬  
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中文摘要:
      针对目前大部分BMP隐写分析方法主要采用单一特征和单一强分类器,容易产生训练样本敏感、分类精度难以提高等问题,提出一种基于特征融合和改进RSM集成分类的BMP图像隐写检测方法. 方法首先串行融合Moulin和SPAM两种经典特征,然后利用序列前向选择(SFS)算法选取分类能力高的特征作为固定特征,其余特征在剩余特征空间中随机抽取,利用固定特征和随机抽取特征构造特征子集,最后在特征子集上训练成员分类器,并用多数投票法对它们进行组合. 实验结果表明:和传统方法相比,在不同嵌入率下,该方法对BMP经典隐写(如LSB匹配、LSB替换、SS和QIM)的检测率均有一定程度的提高.
英文摘要:
      At present,most of the method of BMP steganalysis mainly adopted the single feature and a single strong classifier,which prone to the problems of training samples sensitivity and difficult to improve the classification accuracy. In order to solve these problems,the method of BMP steganalysis is proposed based on feature fusion and improved RSM ensemble. The method firstly serial fusion Moulin feature and SPAM feature. Then selected features which have high classification ability using SFS algorithm as fixed features,the remaining features were selected randomly in the remaining feature space,and then the feature subset was build using fixed features and features selected randomly,finally member classifier was trained on the subset of features,and the finial decision was made by the majority voting procedure. Experimental results show that,in various embedding rates,this method provides more accuracy than the traditional method against steganographic methods of BMP (e.g. LSB matching,LSB replacement,SS and QIM).
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