CLC number: TN918
On-line Access: 2012-08-02
Received: 2011-12-19
Revision Accepted: 2012-03-29
Crosschecked: 2012-07-06
Cited: 12
Clicked: 8894
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]. Journal of Zhejiang University Science C, 2012, 13(8): 624-634.
@article{title="Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis",
author="Song-bin Li, Huai-zhou Tao, Yong-feng Huang",
journal="Journal of Zhejiang University Science C",
volume="13",
number="8",
pages="624-634",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1100374"
}
%0 Journal Article
%T Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis
%A Song-bin Li
%A Huai-zhou Tao
%A Yong-feng Huang
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 8
%P 624-634
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100374
TY - JOUR
T1 - Detection of quantization index modulation steganography in G.723.1 bit stream based on quantization index sequence analysis
A1 - Song-bin Li
A1 - Huai-zhou Tao
A1 - Yong-feng Huang
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 8
SP - 624
EP - 634
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100374
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.
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