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Robust Image Hashing via Random Gabor Filtering and DWT

Zhenjun Tang1,*, Man Ling1, Heng Yao1, Zhenxing Qian2, Xianquan Zhang1, Jilian Zhang3, Shijie Xu1
Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China.
Shanghai Institute of Intelligent Electronics & Systems, School of Computer Science, Fudan University, Shanghai, 200433, China.
School of Information Systems, Singapore Management University, 178902, Singapore.
* Corresponding author: Zhenjun Tang. Email: .

Computers, Materials & Continua 2018, 55(2), 331-344. https://doi.org/10.3970/cmc.2018.02222

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.

Cite This Article

Z. . Tang, M. . Ling, H. . Yao, Z. . Qian, X. . Zhang et al., "Robust image hashing via random gabor filtering and dwt," Computers, Materials & Continua, vol. 55, no.2, pp. 331–344, 2018.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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