A Novel Approach For Image Hashing Using Ring Partition and Invariant Vector Distance

Saba A. Shaikh, Prof. Samadhan A. Sonavane

Abstract


As well famed, image hashing must satisfy two basic properties: Robustness and Discriminative capability (or anticollision capability). Robustness strength means that visually identical pictures have identical (or terribly similar) hash in spite of what their digital representations are. In different words, image hashing ought to be strong against commonly-used digital operations to photographs, like image compression and geometric transforms. Discriminative capability stands for that totally completely different pictures have different image hashes. This means that hash distance between totally different images ought to be massive enough. Certainly, image hashing could produce other properties for coping with specific applications. For example, a key-dependent and robustness to visual content changes once its applied to image authentication .

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References


Saba A. Shaikh, Samadhan A. Sonavane, “Image Hashing for Robustness Discriminative Capability using Ring Partition and Invariant Distance” International Journal of Recent Trends in Engineering Research.January 2017.

Zhenjun Tang, Xianquan Zhang, Xianxian Li, and Shichao Zhang, “Robust Image Hashing With Ring Partition and Invariant Vector Distance” Senior Member, IEEE, January 2016.

Z. Tang, X. Zhang, and S. Zhang, “Robust perceptual image hashing based on ring partition and NMF,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 3, pp. 711724, Mar. 2014.

Y. Zhao, S. Wang, X. Zhang, and H. Yao, “Robust hashing for image Authentication using Zernike moments and local features,” IEEE Trans. Inf. Forensics Security, vol. 8, no. 1, pp. 5563, Jan. 2013.

Z. Tang, X. Zhang, L. Huang, and Y. Dai, “Robust image hashing using Ring-based entropies,” Signal Process., vol. 93, no. 7, pp. 20612069,2013.

E. Hassan, S. Chaudhury, and M. Gopal, “Feature combination in kernel space for distance based image hashing,” IEEE Trans. Multimedia, vol.14, no. 4, pp. 11791195, Aug. 2012.

X. Lv and Z. J. Wang, “Perceptual image hashing based on shape Contexts and local feature points,” IEEE Trans. Inf. Forensics Security, vol. 7, no.3, pp. 10811093, Jun. 2012.

Y. Li, Z. Lu, C. Zhu, and X. Niu, “Robust image hashing based on

random Gabor filtering and dithered lattice vector quantization,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 19631980, Apr. 2012.

C. Qin, C.-C. Chang, and P.-Y. Chen, “Self-embedding fragile watermarking with restoration capability based on adaptive bit allocation mechanism,” Signal Process., vol. 92, no. 4, pp. 11371150, 2012.

Y. Lei, Y. Wang, and J. Huang, “Robust image hash in Radon transform domain for authentication,” Signal Process., Image Commun., vol. 26, no.6, pp. 280288, 2011.

Z. Tang, S. Wang, X. Zhang, W. Wei, and Y. Zhao, “Lexicographical Framework for image hashing with implementation based on DCT and NMF,” Multimedia Tools Appl., vol. 52, nos. 23, pp. 325345, 2011.

W. Lu and M. Wu, “Multimedia forensic hash based on visual words,” in Proc. IEEE Int. Conf. Image Process., Sep. 2010, pp. 989992.

F. Khelifi and J. Jiang, “Perceptual image hashing based on virtual watermark detection,” IEEE Trans. Image Process., vol. 19, no. 4, pp. 981994, Apr. 2010.

F. Ahmed, M. Y. Siyal, and V. U. Abbas, “A secure and robust hash based Scheme for image authentication,” Signal Process., vol. 90, no. 5, pp. 14561470, 2010.

Li-Wei Kang, Chun-Shien Lu,* and Chao-Yung Hsu, “COMPRESSIVE SENSING-BASED IMAGE HASHING” in proc. IEEE Trans. Image Process, pp. 1285-1288, Nov. 2009.

Di Wu , Xuebing Zhou , Xiamu Niu “A novel image hash algorithm resistant to printscan,” Signal Process. vol. 89, no. 12, pp. 2415-2424, Mar. 2009.

Xudong Lv,and Z.jane Wang “Reduced-reference image quality assessment based on percepual image hashing”, 2009-ICIP.

Yang Ou and Kyung Hyune Rhee “A Key-Dependent Secure Image Hashing Scheme by Using Radon Transform”, 2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2009) December 7-9, 2009.

Malcolm Slaney and Michael Casey “Locality-Sensitive Hashing for Finding Nearest Neighbors” IEEE SIGNAL PROCESSING MAGAZINE [128] MARCH 2008.

Ashwin Swaminathan, Student Member, IEEE, Yinian Mao, Student Member, IEEE, and Min Wu, Member, IEEE “Robust and Secure ImageHashing”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 2, JUNE 2006.


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