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Robust Perceptual Image Hashing using SIFT and SVD


Affiliations
1 National Institute of Technology Nagaland, Dimapur, Nagaland 797 103, India
2 National Institute of Technology Manipur, Imphal 795 004, India
 

With the advancement in technology, digital data such as image, video, etc. can be easily manipulated. Image hashing is a method that can be used for authentication and identification of digital images. In this communication, a robust image hashing technique is proposed using scale invariant feature transform (SIFT), singular value decomposition (SVD) and Zernike moment. Zernike moment is used to restore the image against the rotation attack. Potential points are generated from the image by using SIFT. Block processing of equal size is performed on the input image. Key points within the same block are used to generate hashing values. The experimental outcome shows that the proposed technique can withstand different types of attacks. Receiver operating characteristic curve comparison specifies that our method outperforms other existing methods under consideration.

Keywords

Perceptual Hashing Function, Robustness, SIFT, SVD, Zernike Moment.
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  • Schneier, B., Applied Cryptography, Protocols, Algorithms and Source Code in C, John Wiley, Chichester, 1996, 2nd edn.
  • Weng, I. and Preneel, B., From image hashing to video hashing, In 16th International Multimedia Modeling Conference, 2010, 5916, 662–668.
  • Choi, Y. S. and Park, J. Y., Image hash generation method using hierarchical histogram. Multimedia Tools Appl., 2012, 61(1), 181– 194.
  • Tang, Z. Zhang, X. Dai, X. Yang, J. and Wu, T., Robust image hash function using local color features. AEU-Int. J. Electron. Commun., 2013, 67, 717–722.
  • Quyang, J., Liu, Y. and Shu, H., Robust hashing for image authentication using SIFT feature and quaternion Zernike moments. Multimed Tools Appl., 2017, 72(2), 2609–2626.
  • Tang, Z., Yang, F., Huang, L. and Zhang, X., Robust image hashing with dominant DCT coefficients. Optik, 2014, 125(18), 5102–5107.
  • Lin, C.-Y. and Chang, S.-F., A robust image authentication method distinguishing JPEG compression from malicious manipulation. IEEE Trans. Circuits Syst. Video Technol., 2001, 11(2), 151–169.
  • Tang, Z., Zhang, X., Dai, Y. and Lan, W., Perceptual image hashing using local entropies and DWT. Imaging Sci. J., 2013, 61, 241–251.
  • Qin, Q., Chang, C. C. and Tsou, P. L., Robust image hashing using non-uniform sampling in discrete Fourier domain. Digital Signal Proc., 2013, 23(2), 578–585.
  • Neelima, A. and Manglem, Kh., Perceptual hash function based on scale-invarient feature transform and singular value decomposition. Comput. J., 2015 (Online); doi:10.1093/comjnl/bxv079 11. Monga, V. and Mihcak, M. K., Robust and secure image hashing via non-negative matrix factorization. IEEE Trans. Inform. Forens. Secur., 2007, 2(3), 376–390.
  • Lowe, D. G., Distinctive image features from scale invariant keypoints. Int. J. Comput. Vision, 2004, 60, 91–110.

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  • Robust Perceptual Image Hashing using SIFT and SVD

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Authors

Kh. Motilal Singh
National Institute of Technology Nagaland, Dimapur, Nagaland 797 103, India
Arambam Neelima
National Institute of Technology Nagaland, Dimapur, Nagaland 797 103, India
T. Tuithung
National Institute of Technology Nagaland, Dimapur, Nagaland 797 103, India
Kh. Manglem Singh
National Institute of Technology Manipur, Imphal 795 004, India

Abstract


With the advancement in technology, digital data such as image, video, etc. can be easily manipulated. Image hashing is a method that can be used for authentication and identification of digital images. In this communication, a robust image hashing technique is proposed using scale invariant feature transform (SIFT), singular value decomposition (SVD) and Zernike moment. Zernike moment is used to restore the image against the rotation attack. Potential points are generated from the image by using SIFT. Block processing of equal size is performed on the input image. Key points within the same block are used to generate hashing values. The experimental outcome shows that the proposed technique can withstand different types of attacks. Receiver operating characteristic curve comparison specifies that our method outperforms other existing methods under consideration.

Keywords


Perceptual Hashing Function, Robustness, SIFT, SVD, Zernike Moment.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi8%2F1340-1344