A high capacity and strong robust fingerprinting for compressed images

  • Authors:

  • Xinwei Li;Baolong Guo;Long Chen;Xianxiang Wu;Leida Li

  • Affiliations:

  • Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, China;Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, China;Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, China;Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • Venue:
  • Computers and Electrical Engineering
  • Year:
  • 2012

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Abstract

Digital fingerprinting could trace the data source of illegal distribution effectively. Most existing algorithms are only adapted to uncompressed images, whose application fields are limited. In the paper a digital fingerprinting algorithm based on non-subsampled contourlet transform (NSCT) for compressed images is proposed. It is devoted to high capacity and strong robustness for compressed images fingerprinting. The NSCT low frequency coefficients of compressed images are more suitable for hiding information than DCT coefficients, and they are used to construct the high dimension host vector to hide Gaussian fingerprints. Through increasing the dimension of the host vector, on one hand the fingerprinting capacity improves fundamentally, on the other hand the ability of anti-collusion attack enhances greatly. Large experimental results shown that the proposed algorithm proves the declared performance compared with the existing algorithms.