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Multimedia Tools and Applications

, Volume 75, Issue 8, pp 4639–4667 | Cite as

Perceptual image hashing using center-symmetric local binary patterns

  • Reza Davarzani
  • Saeed Mozaffari
  • Khashayar Yaghmaie
Article

Abstract

Perceptual image hashing finds increasing attention in several multimedia security applications such as image identification/authentication, tamper detection, and watermarking. Robust feature extraction is the main challenge in hashing schemes. Local binary pattern (LBP) is a new feature which is due to its simplicity, discriminative power, computational efficiency, and robustness to illumination changes has been used in various image applications. In this paper, we propose a robust image hashing scheme using center-symmetric local binary patterns (CSLBP). In the proposed image hashing, CSLBP features are extracted from each non-overlapping block within the original gray-scale image. For each block, the final hash code is obtained by inner product of its CSLBP feature vector and a pseudorandom weight vector. Furthermore, singular value decomposition (SVD) is combined with CSLBP to introduce a more robust hashing method called SVD-CSLBP. Performances of the proposed hashing schemes are evaluated with two groups of popular applications in perceptual image hashing schemes: image identification and image authentication. Experimental results show that the proposed methods are robust to a wide range of distortions and attacks such as additive noise, blurring, brightness changes and JPEG compression. Moreover, the proposed methods have this capability to localize the tampering area, which is not possible in all hashing schemes.

Keywords

Center-symmetric local binary patterns Perceptual image hashing Singular value decomposition (SVD) Tamper detection 

Notes

Acknowledgment

The authors are grateful for the anonymous reviewers’ insightful comments and valuable suggestions sincerely. We would like appreciate Dr. Xudong Lv, Dr. Vishal Monga and Dr. Divyanshu Vats for letting us to use their codes for comparing the results.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Reza Davarzani
    • 1
  • Saeed Mozaffari
    • 1
  • Khashayar Yaghmaie
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringSemnan UniversitySemnanIran

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