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author:

Zhong, Shang-Ping (Zhong, Shang-Ping.) [1] (Scholars:钟尚平) | Xu, Qiao-Fen (Xu, Qiao-Fen.) [2] | Guo, Wen-Zhong (Guo, Wen-Zhong.) [3] (Scholars:郭文忠) | Liao, Bin (Liao, Bin.) [4]

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Abstract:

By analyzing and proving the correlation between the detection capability of a short duplicate code statistical feature and the probability of cumulating short duplicate codes, the dimension of short duplicate codes, a method to improving the detection capability of a short duplicate code statistical feature was found. Then, a blind detection method for image steganography using short duplicate codes statistical model for Hilbert scanning sequences was proposed. The proposed method used Poisson distribution test to detect the stego-message based on the statistical feature of short duplicate code with same elements in LSB Hilbert scanning sequences. So, the proposed method could make full use of Hilbert curve to maintain the good properties of local correlation, and could not only use the correlation of adjacent elements, but also use the correlation of elements in local regions. Theoretical analysis and experiments show that the proposed method can effectively improve the detection rate under the condition of effectively controlling the false alarm rate.

Keyword:

Codes (symbols) Feature extraction Poisson distribution Scanning Statistics Steganography

Community:

  • [ 1 ] [Zhong, Shang-Ping]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
  • [ 2 ] [Xu, Qiao-Fen]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
  • [ 3 ] [Guo, Wen-Zhong]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China
  • [ 4 ] [Liao, Bin]College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China

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Source :

Journal on Communications

ISSN: 1000-436X

CN: 11-2102/TN

Year: 2013

Issue: 1

Volume: 34

Page: 51-60

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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