Skip to main content
SpringerLink
Log in

CNN Algorithms for Video Authentication and Copyright Protection

  • Published:
Journal of VLSI signal processing systems for signal, image and video technology Aims and scope Submit manuscript

Abstract

This paper is concerned with the implementation of an image comparison algorithm and cryptographic hash functions on the regular array of cellular neural network (CNN) cells. These operations are necessary for message authentication and copyright protection in the multimedia service environment, serving both the interest of the copyright owner and the user. The basic problems of multimedia protection along with the requirements and role of the different actors in a multimedia service environment are also described here.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. B. Preneel, “Analysis and design of cryptographic hash functions,” Doct Dissertation KULeuven, 1993.

  2. A.J. Menezes, P.C. van Oorschot, and S.A. Vanstone, “Handbook of applied cryptography,” CRC Press, Oct. 1996.

  3. B.M. Macq and J-J. Quisquater, “Cryptology for digital TV broadcasting,” Proc. of the IEEE, Vol. 83, No. 6, pp. 944–957, 1995.

    Article  Google Scholar 

  4. OKAPI ACTS project number AC051, D11: Common Functional Model, Feb. 1996.

  5. TALISMAN ACTS project number AC019, D12: Common functional model, March 1996.

  6. B. Preneel, M. Csapodi, and J. Vandewalle, “Cryptographic algorithms: Basic concepts and applications to multimedia security,” in Proc. of the 13th European Conference on Circuit Theory and Design (ECCTD'97), Budapest, Hungary, Aug. 31-Sep. 3, pp. 60–87, 1997.

  7. J. Vandewalle, B. Preneel, and M. Csapodi, “Data security issues, cryptographic protection methods, and the use of cellular neural networks and cellular automata,” in Proc. of the Fifth IEEE Int. Workshop on Cellular Neural Networks and Their Applications (CNNA-98), London, UK, pp. 39–44, April 14-17, 1998.

  8. M. Csapodi, B. Preneel, J. Vandewalle, and T. Roska, “Applications of CNN-UM chips in multimedia authentication,” ESAT/SISTA-COSIC Report TR 97-1, Department of Electrical Engineering-ESAT, Katholieke Universiteit Leuven, 1997.

  9. L.O. Chua and L. Yang, “Cellular neural networks: Theory and applications,” IEEE Trans. Circuits and Systems, Vol. 35, No. 10, pp. 1257–1290, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  10. L.O. Chua and T. Roska, “The CNN paradigm,” IEEE Trans. Circuits and Systems-I, Vol. 40, No. 3, pp. 147–156, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  11. L.O. Chua, “CNN-A vision of complexity,” Int. Journal of Bifurcation and Chaos, Vol. 7, pp. 2219–2425, 1997.

    Article  MathSciNet  MATH  Google Scholar 

  12. T. Roska and L.O. Chua, “The CNN Universal Machine: Ananalogic array computer,” IEEE Trans. Circuits and Systems-II, Vol. 40, pp. 163–173, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  13. S. Espejo, R. Domínguez-Castro, G. Liñán, and Á. Rodríguez-Vázquez, “A 64 × 64 CNNuniversal chip with analog and digital I/O,” in Proc. 5th Int. Conf. on Electronics, Circuits and Systems (ICECS-98), Lisbon, Portugal, pp. 203–206, 7-10 Sept. 1998.

  14. B. Macq and I. Pitas (Ed.), “Special issue on watermarking,” Signal Processing, Vol. 66, No. 3, 1998.

  15. I.J. Cox, J. Kilian, T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” NEC Research Institute, Technical Report 95-10, 1995.

  16. H. Berghel and L. O'Gorman, “Protecting ownership rights through digital watermarking,” IEEE Computer, Vol. 29, No. 7, pp. 101–103, 1996.

    Article  Google Scholar 

  17. J.L. Carter and M.N. Wegman, “Universal classes of hash functions,” Journal of Computer and System Sciences, Vol. 18, No. 2, pp. 143–154, 1979.

    Article  MathSciNet  MATH  Google Scholar 

  18. Y. Mansour, N. Nisan, and P. Tiwari, “The computational complexity of universal hashing,” Theoretical Computer Science, Vol. 107, No. 1, pp. 121–133, 1993.

    Article  MathSciNet  MATH  Google Scholar 

  19. B. den Boer, “A simple and key-economical unconditional authentication scheme,” Journal of Computer Security, Vol. 2, No. 1, pp. 65–71, 1993.

    Google Scholar 

  20. G.A. Kabatianskii, B. Smets, and T. Johansson, “On the cardinality of systematic authentication codes via error-correcting codes,” IEEE Trans. on Inf. Theory, Vol. 42, No. 2, 1996.

  21. O. Martin, A.M. Odlyzko, and S. Wolfram, “Algebraic properties of cellular automata,” Commun. Math. Phys., Vol. 93, pp. 219–258, 1984, reprinted in S. Wolfram (Ed.), Theory and Applications of Cellular Automata, World Scientific, pp. 51-90, 1986.

    Article  MathSciNet  MATH  Google Scholar 

  22. M. Csapodi, J. Vandewalle, and T. Roska, “Invertible operations on a cellular neural network universal machine-Based on the implementation of two-dimensional cellular automata,” Int. Journal of Circuit Theory and Applications, Vol. 26, pp. 611–634, 1998.

    Article  MATH  Google Scholar 

  23. K. László, F. Ziliani, T. Roska, and M. Kunt, “Early segmentation in video compression using CNN processors,” in Proc. of the Fifth IEEE Int.Workshop on Cellular Neural Networks and their Applications (CNNA-98), London, UK, pp. 175–180, April 14-17, 1998.

  24. A. Stoffels, T. Roska, and L.O. Chua, “Object-oriented image analysis for very-low-bitrate video-coding systems using the CNN universal machine,” Int. Journal of Circuit Theory and Applications, Vol. 25, pp. 235–258, 1997.

    Article  Google Scholar 

  25. T. Szirányi and J. Zerubia, “Markov random field image segmentation using cellular neural network,” IEEE Trans. Circuits and Systems-I, Vol. 44, pp. 86–89, 1997.

    Article  Google Scholar 

  26. Á. Zarándy, A. Stoffels, T. Roska, F. Werblin, and L.O. Chua, “Implementation of binary and grayscale mathematical morphology on the CNN universal machine,” IEEE Transactions on Circuits and Systems, Vol. 45, No. 2, pp. 163–168, 1998.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Analogic and Neural Computing Systems Laboratory, Computer and Automation Institute, Hungarian Academy of Sciences, P.O.B.63, H-1502, Budapest, Hungary

    Márton Csapodi

  2. ESAT SISTA/COSIC Laboratory, Department of Electrical Engineering, Katholieke Universiteit Leuven, K. Mercierlaan 94, B-3001, Leuven, Belgium

    Joos Vandewalle & Bart Preneel

Authors
  1. Márton Csapodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joos Vandewalle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bart Preneel
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Csapodi, M., Vandewalle, J. & Preneel, B. CNN Algorithms for Video Authentication and Copyright Protection. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 23, 449–463 (1999). https://doi.org/10.1023/A:1008113606327

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008113606327

Keywords

Search

Navigation