Reversible semi-fragile image authentication using zernike moments and integer wavelet transform

  • Authors:

  • Xiaoyun Wu;Xiaoping Liang;Hongmei Liu;Jiwu Huang;Guoping Qiu

  • Affiliations:

  • School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China;School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China;School of Computer Science, University of Nottingham, UK

  • Venue:
  • DRMTICS'05 Proceedings of the First international conference on Digital Rights Management: technologies, Issues, Challenges and Systems
  • Year:
  • 2005

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Abstract

Semi-fragile image authentication based on watermarking has drawn extensive attention. However, conventional watermarking techniques introduce irreversible distortions to the host signals and thus may not be allowable in some applications such as medical and military imaging. Though some reversible fragile authentication algorithms had been developed, reversible semi-fragile authentication presents a challenge. To the best of our knowledge, so far there are only two reversible semi-fragile authentication algorithms based on watermarking reported in the literature. The existing reversible semi-fragile authentication schemes have two shortcomings: i) Watermark security has not received attention; ii) They have weak capability to resist JPEG compression. In this paper, we propose a novel reversible semi-fragile image authentication scheme. The algorithm can distinguish malicious modification from incidental modification according to semi-fragile characteristics of Zernike moments magnitudes (ZMMs) of the low frequency subband in integer wavelet transform (IWT) domain of an image. Combining semi-fragile characteristics of ZMMs, the watermark can discern forgery attack, thus, improving watermark security. The algorithm can locate the tampered area of an image accurately while tolerating JPEG lossy compression at a low quality factor. Experimental results demonstrate the merits of the proposed algorithm.