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Robust Content-Based Image Hash Functions Using Nested Lattice Codes

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Digital-Forensics and Watermarking (IWDW 2015)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9569))

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Abstract

This contribution improves content-based hash functions for image retrieval systems using nested lattice codes. Lattice codes are used to quantize image feature vectors to final hash values. The goal is to develop a nested lattice indexing scheme such that there is a proportional relationship between Euclidean distance and some metric distances (Hamming distance or, as in this paper, weighted Hamming distance and first difference distance) in order to increase the hash function’s robustness. The proposed two-dimensional nested lattice code reduces the normalized mean squared error (NMSE) by 20 % compared to two-dimensional Gray code.

B.M. Kurkoski—This work was supported by JSPS Kakenhi Grant Number 26289119.

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References

  1. Menezes, A.J., Van Oorschot, P.C., Vanstone, S.A.: Handbook of Applied Cryptography. CRC Press, Boca Raton (1996)

    Book  MATH  Google Scholar 

  2. Huang, C.-L., Huang, D.-H.: A content-based image retrieval system. Image Vis. Comput. 16(3), 149–163 (1998)

    Article  Google Scholar 

  3. Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)

    Chapter  Google Scholar 

  4. Venkatesan, R., et al.: Robust image hashing. In: Proceedings, 2000 International Conference on Image Processing, vol. 3. IEEE (2000)

    Google Scholar 

  5. Hernandez, R.A.P., Miyatake, M.N., Kurkoski, B.M.: Robust image hashing using image normalization and SVD decomposition. In: 2011 IEEE 54th Midwest Symposium on MWSCAS. IEEE (2011)

    Google Scholar 

  6. Faloutsos, C.: Gray codes for partial match and range queries. IEEE Trans. Softw. Eng. 14(10), 1381–1393 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  7. Guopu, Z., et al.: Fragility analysis of adaptive quantization-based image hashing. IEEE Trans. Inf. Forensics Secur. 5(1), 133–147 (2010)

    Article  Google Scholar 

  8. Li, Y., et al.: Robust image hashing based on random Gabor filtering and dithered lattice vector quantization. IEEE Trans. Image Process. 21, 1963–1980 (2012)

    Article  MathSciNet  Google Scholar 

  9. Conway, J.H., Sloane, N.J.A.: Sphere Packings, Lattices and Groups, 3rd edn. Springer, New York (1999)

    Book  MATH  Google Scholar 

  10. Gray, R.M.: Vector quantization. IEEE ASSP Mag. 1(2), 4–29 (1984)

    Article  Google Scholar 

  11. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Conway, J.H., Sloane, N.J.A.: A fast encoding method for lattice codes and quantizers. IEEE Trans. Inf. Theory IT–29, 820–824 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  13. Gray, F.: Pulse code communication. U.S. Patent, 17 March 1953

    Google Scholar 

  14. Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Thanh Xuan Nguyen, Ricardo A. Parrao Hernandez & Brian M. Kurkoski

Authors
  1. Thanh Xuan Nguyen
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  2. Ricardo A. Parrao Hernandez
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  3. Brian M. Kurkoski
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Corresponding author

Correspondence to Thanh Xuan Nguyen .

Editor information

Editors and Affiliations

  1. NJIT, Newark, New Jersey, USA

    Yun-Qing Shi

  2. Korea University, Seoul, Korea (Republic of)

    Hyoung Joong Kim

  3. University of Vigo, Vigo, Spain

    Fernando Pérez-González

  4. National Institute of Informatics, Tokyo, Japan

    Isao Echizen

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Nguyen, T.X., Hernandez, R.A.P., Kurkoski, B.M. (2016). Robust Content-Based Image Hash Functions Using Nested Lattice Codes. In: Shi, YQ., Kim, H., Pérez-González, F., Echizen, I. (eds) Digital-Forensics and Watermarking. IWDW 2015. Lecture Notes in Computer Science(), vol 9569. Springer, Cham. https://doi.org/10.1007/978-3-319-31960-5_33

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  • DOI: https://doi.org/10.1007/978-3-319-31960-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31959-9

  • Online ISBN: 978-3-319-31960-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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