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Exploring better parameter set for singular value decomposition (SVD) hashing function used in image authentication

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3 Author(s)
Albert B. Jeng ; Department of computer science and information engineer, Jinwen University of Science and Technology, Taipei, Taiwan ; Li-Chung Chang ; Hong-Jhe Li

This paper makes use of a useful image hashing program tool by Vishal Monga to explore a better parameter set for singular value decomposition (SVD) hashing function used in image authentication. One of the functions provided in Monga's tool is a SVD based image hashing which currently uses a predefine parameter set to compute the image hashing. However, Monga's approach starts with an arbitrary threshold constant of 0.02 value and other predefined parameter setting (e.g. partition size, sub-image size, and eigenvector number) which may not be optimal or suitable for generating a secure and robust image hashing for all general images. We try to explore a different parameter sets for this SVD image hashing algorithm in order to enhance the robustness and security of this algorithm. We show our experiment result in the appendix. It shows the optimal parameter set derived by us has a better performance than Monga's predefined set. It also shows a more secure and robust image authentication when applying to the standard test images provided by the USC-SIPI image database.

Published in:

2010 International Conference on Machine Learning and Cybernetics  (Volume:5 )

Date of Conference:

11-14 July 2010