International Journal of Wireless and Microwave Technologies(IJWMT)

ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)

Published By: MECS Press

IJWMT Vol.1, No.5, Oct. 2011

Geometric Invariant Robust Image Hashing Via Zernike Moment

Full Text (PDF, 227KB), PP.9-15

Views:78   Downloads:1


Rui Sun,Xiaoxing Yan,Wenjun Zeng

Index Terms

Zernike moment; Geometric invariant; image hashing ; image indexing; robustness


Robust image hashing methods require the robustness to content preserving processing and geometric transform. Zernike moment is a local image feature descriptor whose magnitude components are rotationally invariant and most suitable for image hashing application. In this paper, we proposed Geometric invariant robust image hashing via zernike momment. Normalized zernike moments of an image are used as the intermediate hash. Rotation invariance is achieved by taking the magnitude of the zernike moments. Image normalization method is used for scale and translation invariance. A randomization diffusion processing enhance hashing security. The test results show that our method is robust with respect to the geometrical distortions and content preserving processing.

Cite This Paper

Rui Sun,Xiaoxing Yan,Wenjun Zeng,"Geometric Invariant Robust Image Hashing Via Zernike Moment", IJWMT, vol.1, no.5, pp.9-15, 2011.


[1]V.Monga, Perceptually based methods for robust image hashing, Ph.D thesis, University of Texas, 2005.

[2]C.D.Roover, C.D.Vleeschouwer, F.Lefebvre, and B.Macq, "Robust image hashing based on radial variance of pixels," in IEEE International Conference on Image Processing (ICIP'05), 2005, pp. 77–80

[3]X.C.Guo and D.Hatzinakos, "Content based image hashing via wavelet and radon transform," in PCM 2007, 2007, LNCS 4810, pp. 755–764.

[4]A. Swaminathan, Y. Mao, and M. Wu, Robust and secure image hashing, IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, 2006, pp. 215-230.

[5]V. Monga and M. K. Mihcak, Robust and secure image hashing via non-negative matrix factorizations, IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, 2007, pp. 376-390.

[6]C. H. Teh and R. T. Chin, "On image analysis by the methods of moments," IEEE Trans. Pattern Anal. Machine Intell., vol. 10, pp. 496–513, Apr. 1988.

[7]B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada, "Color and texture descriptors," IEEE Trans. Circuits Syst. Video Technol., vol. 11, no. 6, pp. 703–715, Jun. 2001

[8]H. S Kim, H. K Lee. Invariant image watermark using Zernike moments. IEEE Trans. Circ. Syst. Video Tech. 13, 8, 766—775, 2003

[9]P. Dong, J.G. Brankov, N.P. Galatsanos, Y. Yang, and F. Davoine,"Digital Watermarking Robust to Geometric Distortions," IEEE Trans. Image Processing, Vol. 14, No. 12, pp. 2140-2150, 2005