Adjacent-block based statistical detection method for self-embedding watermarking techniques

  • Authors:

  • Hong-Jie He;Jia-Shu Zhang;Fan Chen

  • Affiliations:

  • Sichuan Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China;Sichuan Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China;Information Security and National Computing Grid Lab, Southwest Jiaotong University, Chengdu 610031, China

  • Venue:
  • Signal Processing
  • Year:
  • 2009

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Abstract

This paper proposes an adjacent-block based statistical detection method for self-embedding watermarking techniques to accurately identify the tampered blocks, and gives an analytical analysis of the tamper detection performance. In the proposed statistical detection method, we take all adjacent blocks of the test block and its mapping block into account and then utilize a statistic-based rule to verify the validity of image blocks. Analytical analysis and experimental results demonstrate that the proposed statistical detection method can identify the tampered blocks with a probability more than 98% even the tampered area is up to 70% of the host image. In addition, the proposed method outperforms conventional self-embedding fragile watermarking algorithms in tamper detection under collage attack and content-tampering attack.