计算机工程

• 安全技术 • 上一篇    下一篇

精细化辨识时空特征的视频隐写分析

周红志,王戴木   

  1. (阜阳师范学院信息工程学院,安徽 阜阳 236041)
  • 收稿日期:2013-03-19 出版日期:2014-01-15 发布日期:2014-01-13
  • 作者简介:周红志(1981-),女,讲师、硕士,主研方向:网络信息安全,图形图像处理,数据库技术;王戴木,教授、博士
  • 基金项目:
    安徽省自然科学研究基金资助项目(KJ2013B207)

Video Steganalysis with Refined Identification of Temporal and Spatial Characteristics

ZHOU Hong-zhi, WANG Dai-mu   

  1. (College of Information Engineering, Fuyang Teachers’ College, Fuyang 236041, China)
  • Received:2013-03-19 Online:2014-01-15 Published:2014-01-13

摘要: 视频隐写分析技术可以侦测出含有隐藏秘密信息的视频,为社会安全提供保障。视频除了含有图像内的空间信息,还蕴含着相邻帧图像之间的时间信息。针对这一特点,提出一种精细化辨识时空特征的视频隐写分析方法。该方法对视频在时间和空间维度的特征量进行精细化建模。采用Marcov对视频空间层次上的块内和块间过程进行建模,以提取空间特征量。利用差值分析视频时间层次上的变化,以提取时间特征量,并将时间和空间特征量输入到SVM模型中进行训练和检测。实际测试结果表明,该方法能够有效区分载密视频和非载密视频,对3 100段测试视频样本的检测准确率高达97.13%。

关键词: 视频隐写分析, 时空特征, 马尔科夫过程, 小波变换, 支持向量机, 差值分析

Abstract: Video steganalysis technique can detect the hiding secret information in video, and provide security for national, governmental and corporate secrets. Videos contain not only spatial information within the image, but also the temporal information between the images. This paper proposes a video steganalysis method with refined identification of the temporal and spatial characteristics. The method defines the features of video in temporal and spatial dimension in detail. Marcov technique is utilized to model intra-block and inter-block process of image and extracts spatial feature. Difference analysis is utilized to model the time changing process of image and extracts temporal characteristics. Temporal and spatial characteristics are input to the Support Vector Machine(SVM) model for training and testing. Actual detection precision of 97.13% for 3 100 test videos show that the proposed method can effectively distinguish stego video and non-stego video.

Key words: video steganalysis, temporal and spatial characteristics, Marcov process, wavelet transformation, Support Vector Machine (SVM), difference analysis

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