Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2018
Abstract
Image hashing is a useful multimedia technology for many applications, such as image authentication, image retrieval, image copy detection and image forensics. In this paper, we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform (DWT). Specifically, robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map, and then are compressed via a single-level 2-D DWT. Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band. Many experiments with open image datasets are carried out and the results illustrate that our hashing is robust, discriminative and secure. Receiver operating characteristic (ROC) curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.
Keywords
Image hashing, Gabor filtering, chaotic map, skew tent map, discrete wavelet transform
Discipline
Graphics and Human Computer Interfaces
Publication
Computers, Materials & Continua
Advisors/Committee Chairs
NA
Degree Awarded
PhD in Computer Science
Issue
2
First Page
331
Last Page
344
Identifier
10.3970/cmc.2018.02222
Publisher
Tech Science Press
Citation
TANG, Zhenjun; LING, Man; YAO, Heng; QIAN, Zhenxing; ZHANG, Xianquan; ZHANG, Jilian; and XU, Shijie.
Robust image hashing via random Gabor filtering and DWT. (2018). Computers, Materials & Continua. 55, (2), 331-344.
Available at: https://ink.library.smu.edu.sg/phd_publications_collection/1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3970/cmc.2018.02222
