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A Perfect Hash Model Used for Image Content Tamper Detection

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Advances in Image and Graphics Technologies (IGTA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 634))

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Abstract

An effective image content temper detection scheme that uses perfect hash model is proposed in this paper. Aiming at low detection failing rates and high detection successful rates in image tamper localization, the proposed scheme uses perfect hash model to overcome easy collisions of the random numbers to enhance the effectiveness in authentication procedure. This scheme embeds perfect hash information in the LSBs of the original image to protect image content. The experimental results demonstrate that the proposed scheme has a good performance and can be used for image authentication applications.

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References

  1. Venkatesan, R., Koon, S.M., Jakubowski, M.H., Moulin, P.: Robust image hashing. In: Proceedings of IEEE International Conference on Image Processing, vol. 3, pp. 664–666 (2000)

    Google Scholar 

  2. Fridrich, J., Goljan, M.: Robust hash functions for digital watermarking. In: Proceedings of IEEE International Conference on Information Technology: Coding and Computing, pp. 178–183 (2000)

    Google Scholar 

  3. Tagliasacchi, M., Valenzise, G., Tubaro, S.: Hash-based identification of sparse image tampering. IEEE Trans. Image Process. 18(11), 2491–2504 (2009)

    Article  MathSciNet  Google Scholar 

  4. Ahmed, F., Siyal, M.Y., Uddin Abbas, V.: A secure and robust hash-based scheme for image authentication. Sig. Process. 90(5), 1456–1470 (2010)

    Article  MATH  Google Scholar 

  5. Lei, Y., Wang, Y., Huang, J.: Robust image hash in Radon transform domain for authentication. Sig. Process. Image Commun. 26(6), 280–288 (2011)

    Article  Google Scholar 

  6. Zhao, Y., Wang, S., Zhang, X., Yao, H.: Robust hashing for image authentication using Zernike moments and local features. IEEE Trans. Inf. Forensics Secur. 8(1), 55–63 (2013)

    Article  Google Scholar 

  7. Wu, M., Liu, B.: Watermarking for image authentication. In: Proceedings of IEEE International Conference on Image Processing, ICIP 1998, vol. 2, pp. 437–441 (1998)

    Google Scholar 

  8. Kundur, D., Hatzinakos, D.: Digital watermarking for telltale tamper proofing and authentication. Proc. IEEE 87(7), 1167–1180 (1999)

    Article  Google Scholar 

  9. Yang, L., Ni, R., Zhao, Y.: Segmentation-based image authentication and recovery scheme using reference sharing mechanism. In: 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, pp. 863–866 (2012)

    Google Scholar 

  10. Preda, R.O.: Semi-fragile watermarking for image authentication with sensitive tamper localization in the wavelet domain. Measurement 46(1), 367–373 (2013)

    Article  MathSciNet  Google Scholar 

  11. Chang, C.C., Fan, Y.H., Tai, W.L.: Four-scanning attack on hierarchical digital watermarking method for image tamper detection and recovery. Pattern Recognit. 41(2), 654–661 (2008)

    Article  MATH  Google Scholar 

  12. Rawat, S., Raman, B.: A chaotic system based fragile watermarking scheme for image tamper detection. AEU-Int. J. Electron. Commun. 65(10), 840–847 (2011)

    Article  Google Scholar 

  13. Tong, X., Liu, Y., Zhang, M., Chen, Y.: A novel chaos-based fragile watermarking for image tampering detection and self-recovery. Signal Process.: Image Commun. 28(3), 301–308 (2012)

    Google Scholar 

  14. Lee, C.W., Tsai, W.H.: A data hiding method based on information sharing via PNG images for applications of color image authentication and metadata embedding. Signal Process. 93(7), 2010–2025 (2013)

    Article  Google Scholar 

  15. Sprugnoli, R.: Perfect hashing functions: a single probe retrieving method for static sets. Commun. ACM 20(11), 841–850 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  16. Du, M.W., Hsieh, T.M., Jea, K.F., Shieh, D.W.: The study of a new perfect hash scheme. IEEE Trans. Softw. Eng. SE-9(3), 305–313 (1983)

    Article  MATH  Google Scholar 

  17. Tang, Z., Zhang, X., Dai, X., Yang, J., Wu, T.: Robust image hash function using local color features. AEU-Int. J. Electron. Commun. 67(8), 717–722 (2013)

    Article  Google Scholar 

  18. Byun, S.C., Lee, I.L., Shin, T.H., Ahn, B.H.: A public-key based watermarking for color image authentication. In: IEEE International Conference on Multimedia and Expo, vol. 1, pp. 593–596 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. NARI Technology Development Co., Ltd., Anhui NARI Zenith Electricity & Electronic Co., Ltd., Hefei, 230088, China

    Jing Sun

  2. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, 230039, China

    Wan-Li Lyu

Authors
  1. Jing Sun
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  2. Wan-Li Lyu
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Corresponding author

Correspondence to Wan-Li Lyu .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Peking University, Beijing, China

    Guoping Wang

  3. Tsinghua University, Beijing, China

    Shengjin Wang

  4. Beijing Institute of Technology, Beijing, China

    Yue Liu

  5. Peking University, Beijing, China

    Xiaoru Yuan

  6. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

  7. Peking University, Beijing, China

    Sheng Li

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© 2016 Springer Science+Business Media Singapore

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Cite this paper

Sun, J., Lyu, WL. (2016). A Perfect Hash Model Used for Image Content Tamper Detection. In: Tan, T., et al. Advances in Image and Graphics Technologies. IGTA 2016. Communications in Computer and Information Science, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-10-2260-9_25

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  • DOI: https://doi.org/10.1007/978-981-10-2260-9_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2259-3

  • Online ISBN: 978-981-10-2260-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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