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 rand...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.展开更多
基金This work is partially supported by the National Natural Science Foundation of China(Nos.61562007,61762017,61702332)National Key R&D Plan of China(2018YFB1003701)+3 种基金Guangxi“Bagui Scholar”Teams for Innovation and Research,the Guangxi Natural Science Foundation(Nos.2017GXNSFAA198222,2015GXNSFDA139040)the Project of Guangxi Science and Technology(Nos.GuiKeAD17195062)the Project of the Guangxi Key Lab of Multi-source Information Mining&Security(Nos.16-A-02-02,15-A-02-02)the Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing,and the Innovation Project of Guangxi Graduate Education(No.XYCSZ 2018076).
文摘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.
