International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 1, No. 5, 15 Oct. 2011
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Geometric Invariant Robust Image Hashing Via Zernike Moment
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Author(s)
Rui Sun
1,*
Xiaoxing Yan
1
Wenjun Zeng
2
Index Terms
Zernike moment, Geometric invariant, image hashing, image indexing, robustness
Abstract
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. DOI: 10.5815/ijwmt.2011.05.02
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