A Secure Perceptual Hash Algorithm for Image Content Authentication

  • Li Weng
  • Bart Preneel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7025)


Perceptual hashing is a promising solution to image content authentication. However, conventional image hash algorithms only offer a limited authentication level for the protection of overall content. In this work, we propose an image hash algorithm with block level content protection. It extracts features from DFT coefficients of image blocks. Experiments show that the hash has strong robustness against JPEG compression, scaling, additive white Gaussian noise, and Gaussian smoothing. The hash value is compact, and highly dependent on a key. It has very efficient trade-offs between the false positive rate and the true positive rate.


Additive White Gaussian Noise True Positive Rate Image Block JPEG Compression Message Authentication Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Menezes, A., van Oorschot, P., Vanstone, S.: Handbook of Applied Cryptography. CRC Press, Boca Raton (1996)CrossRefzbMATHGoogle Scholar
  2. 2.
    Coskun, B., Memon, N.: Confusion/diffusion capabilities of some robust hash functions. In: Proc. of 40th Annual Conference on Information Sciences and Systems, Princeton, USA (March 2006)Google Scholar
  3. 3.
    Weng, L., Preneel, B.: Attacking some perceptual image hash algorithms. In: Proc. of IEEE International Conference on Multimedia & Expo., pp. 879–882 (2007)Google Scholar
  4. 4.
    Voloshynovskiy, S., Koval, O., Beekhof, F., Pun, T.: Conception and limits of robust perceptual hashing: towards side information assisted hash functions. In: Proc. of SPIE., vol. 7254 (February 2009)Google Scholar
  5. 5.
    Weng, L., Preneel, B.: Shape-based features for image hashing. In: Proc. of IEEE International Conference on Multimedia & Expo. (2009)Google Scholar
  6. 6.
    Schneider, M., Chang, S.F.: A robust content based digital signature for image authentication. In: Proc. of International Conference on Image Processing (ICIP 1996), vol. 3, pp. 227–230 (1996)Google Scholar
  7. 7.
    Fridrich, J.: Robust bit extraction from images. In: Proc. of IEEE International Conference on Multimedia Computing and Systems, vol. 2, pp. 536–540 (1999)Google Scholar
  8. 8.
    Fridrich, J., Goljan, M.: Robust hash functions for digital watermarking. In: Proc. of International Conference on Information Technology: Coding and Computing (2000)Google Scholar
  9. 9.
    Venkatesan, R., Koon, S.M., Jakubowski, M., Moulin, P.: Robust image hashing. In: Proc. of IEEE International Conference on Image Processing, Vancouver, CA, vol. 3, pp. 664–666 (2000)Google Scholar
  10. 10.
    Mihçak, M.K., Venkatesan, R.: New iterative geometric methods for robust perceptual image hashing. In: Proceedings of ACM Workshop on Security and Privacy in Digital Rights Management, Philadelphia, PA, USA (November 2001)Google Scholar
  11. 11.
    Monga, V., Evans, B.: Robust perceptual image hashing using feature points. In: Proc. of IEEE International Conference on Image Processing, vol. 1, pp. 677–680 (2004)Google Scholar
  12. 12.
    Lefèbvre, F., Macq, B., Legat, J.D.: RASH: RAdon Soft Hash algorithm. In: Proc. of the 11th European Signal Processing Conference, Toulouse, France, vol. 1, pp. 299–302 (September 2002)Google Scholar
  13. 13.
    Swaminathan, A., Mao, Y., Wu, M.: Robust and secure image hashing. IEEE Transactions on Information Forensics and Security 1(2), 215–230 (2006)CrossRefGoogle Scholar
  14. 14.
    Swaminathan, A., Mao, Y., Wu, M.: Security of feature extraction in image hashing. In: Proc. of 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA (March 2005)Google Scholar
  15. 15.
    Radhakrishnan, R., Xiong, Z., Memon, N.: On the security of the visual hash function. Journal of Electronic Imaging 14, 10 (2005)Google Scholar
  16. 16.
    Oppenheim, A., Lim, J.: The importance of phase in signals. Proceedings of the IEEE 69(5), 529–541 (1981)CrossRefGoogle Scholar
  17. 17.
    Gegenfurtner, K., Braun, D., Wichmann, F.: The importance of phase information for recognizing natural images. Journal of Vision 3(9), 519a (2003)CrossRefGoogle Scholar
  18. 18.
    Ni, X., Huo, X.: Statistical interpretation of the importance of phase information in signal and image reconstruction. Statistics & Probability Letters 77(4), 447–454 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Barker, E., Kelsey, J.: Recommendation for random number generation using deterministic random bit generators. Technical report, NIST (2007)Google Scholar
  20. 20.
    Johnson, M., Ramchandran, K.: Dither-based secure image hashing using distributed coding. In: Proc. of IEEE International Conference on Image Processing, vol. 2, pp. 751–754 (2003)Google Scholar
  21. 21.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRefGoogle Scholar
  22. 22.
    Mao, Y., Wu, M.: Unicity distance of robust image hashing. IEEE Transactions on Information Forensics and Security 2(3), 462–467 (2007)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Li Weng
    • 1
  • Bart Preneel
    • 1
  1. 1.ESAT/COSIC-IBBTKatholieke Universiteit LeuvenBelgium

Personalised recommendations