Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis

Song-bin LI; Huai-zhou TAO; Yong-feng HUANG

doi:10.1631/jzus.C1100374

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Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis

Electronic Engineering | Updated：2022-12-09

- Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis
- Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 8, Pages: 624-634(2012)
- Affiliations：
  
  1.Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
  2.Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
- Author bio：
  
  [ "Song-bin LI, E-mail:lisb@mail.tsinghua.edu.cn" ]
  Yong-feng HUANG, E-mail: yfhuang@mail.tsinghua.edu.cn
- Funds：
- DOI：10.1631/jzus.C1100374
  CLC： TN918
- Received：19 December 2011，
  
  Revised：2012-;7-;6，
  
  Published：2012-08
- Accepted：
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Song-bin LI, Huai-zhou TAO, Yong-feng HUANG. Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(8): 624-634.
DOI：

Song-bin LI, Huai-zhou TAO, Yong-feng HUANG. Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(8): 624-634. DOI： 10.1631/jzus.C1100374.

摘要

Abstract

This paper presents a method to detect the quantization index modulation (QIM) steganography in G.723.1 bit stream. We show that the distribution of each quantization index (codeword) in the quantization index sequence has unbalanced and correlated characteristics. We present the designs of statistical models to extract the quantitative feature vectors of these characteristics. Combining the extracted vectors with the support vector machine

we build the classifier for detecting the QIM steganography in G.723.1 bit stream. The experiment shows that the method has far better performance than the existing blind detection method which extracts the feature vector in an uncompressed domain. The recall and precision of our method are all more than 90% even for a compressed bit stream duration as low as 3.6 s.

关键词

Keywords

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