Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Swarming with (visual) secret (shared) mission

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Collaborative secure image matching is a problem that is applicable in various domains, for both—data in rest and data in motion. The problem is defined as follows. There is a secret image, and a set of n mobile agents. The set of mobile agents should match (compare) an observed image to the original secret image. In this paper we discuss some of the existing approaches, and present an alternative solution applied and analyzed for different applications. The first application is a swarm of Unmanned Aerial Vehicles that search for a target specified by an image. The second application is a social network that serves as a smart storage device capable of performing distributed, secret image matching operations. Our solution is based on the well-known Visual Encryption Scheme and projections of visual bit maps rather than (quadratic complexity) messages exchange in implementing Secure Multi Party Computation scheme. We present a perfect-information-theoretic secure solution for this problem. To keep the original image secrecy, at least k out of n mobile agents are required to retrieve any information about the original image.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Algorithm 2
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability statement

Our manuscript does not have associated data.

Notes

  1. One may consider RGB images by handling the matrix corresponding to threshold R with values 0 when the pixel has no red component and 1 otherwise. Similarly, to green and blue components of the pixel, thus matching three binary matrices instead of one. More sophisticated schemes for fine tuned colors can be supported by adding more matrices.

References

  1. Albrecht, M. R., Grassi, L., Perrin, L., Ramacher, S., Rechberger, C., Rotaru, D., Roy, A., & Schofnegger, M. (2019). Feistel structures for mpc, and more. In European symposium on research in computer security (pp. 151–171). Springer.

  2. Beaver, D. (1991). Efficient multiparty protocols using circuit randomization. In Annual international cryptology conference (pp. 420–432). Springer.

  3. Beaver, D., Micali, S., & Rogaway, P. (1990). The round complexity of secure protocols. In Proceedings of the twenty-second annual ACM symposium on theory of computing (pp. 503–513).

  4. Ben-Or, M., Goldwasser, S., & Wigderson, A. (2019). Completeness theorems for non-cryptographic fault-tolerant distributed computation (pp. 351–371). New York, NY, USA: Association for Computing Machinery.

    Google Scholar 

  5. Berend, D., Dolev, S., Frenkel, S., & Hanemann, A. (2016). Towards holographic “brain’’ memory based on randomization and Walsh-Hadamard transformation. Neural Networks, 77, 87–94.

    Article  Google Scholar 

  6. Bruckstein, A. M., Holt, R. J., & Netravali, A. N. (1998). Holographic representations of images. IEEE Transactions on Image Processing, 7(11), 1583–1597.

    Article  Google Scholar 

  7. Chaum, D., Crépeau, C., & Damgard, I. (1988). Multiparty unconditionally secure protocols. In Proceedings of the twentieth annual ACM symposium on theory of computing (pp. 11–19).

  8. Dolev, D., & Raymond Strong, H. (1983). Authenticated algorithms for byzantine agreement. SIAM Journal on Computing, 12(4), 656–666.

    Article  MathSciNet  Google Scholar 

  9. Dolev, S., & Frenkel, S. (2010) Multiplication free holographic coding. In 2010 IEEE 26th convention of electrical and electronics engineers in Israel (pp. 000146–000150). IEEE

  10. Frangos, M., & Jaimoukha, I. M. (2008). Adaptive rational interpolation: Arnoldi and Lanczos-like equations. European Journal of Control, 14(4), 342–354.

    Article  MathSciNet  Google Scholar 

  11. Gennaro, R., Rabin, M. O., & Rabin, T. (1998). Simplified VSS and fast-track multiparty computations with applications to threshold cryptography. In Proceedings of the seventeenth annual ACM symposium on principles of distributed computing, PODC ’98 (pp. 101–111). New York, NY, USA: Association for Computing Machinery.

  12. Getreuer, P. (2013). A survey of Gaussian convolution algorithms. Image Processing On Line, 286–310, 2013.

    Google Scholar 

  13. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587).

  14. Goldreich, O., Micali, S., & Wigderson, A. (1987). How to play any mental game. In Proceedings of the nineteenth annual ACM symposium on theory of computing, STOC ’87 (pp. 218–229). New York, NY, USA: Association for Computing Machinery.

  15. Goldreich, O., Micali, S., & Wigderson, A. (2019). How to play any mental game, or a completeness theorem for protocols with honest majority (pp. 307–328). New York, NY, USA: Association for Computing Machinery.

    Google Scholar 

  16. Goldwasser, S., Ben-Or, M. & Wigderson, A. (1988). Completeness theorems for non-cryptographic fault-tolerant distributed computing. In Proceedings of the 20th STOC (pp. 1–10).

  17. Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9), 850–863.

    Article  Google Scholar 

  18. Jiao, S., Feng, J., Gao, Y., Lei, T., & Yuan, X. (2020). Visual cryptography in single-pixel imaging. Optics Express, 28(5), 7301–7313.

    Article  Google Scholar 

  19. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1725–1732).

  20. Kulkarni, S. R., Lugosi, G., & Venkatesh, S. S. (1998). Learning pattern classification-A survey. IEEE Transactions on Information Theory, 44(6), 2178–2206.

    Article  MathSciNet  Google Scholar 

  21. Liu, J., Gan, Z., Zhu, X. (2013). Directional bicubic interpolation—A new method of image super-resolution. In Proceedings of 3rd international conference on multimedia technology (ICMT-13) (pp. 463–470). Atlantis Press.

  22. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431–3440).

  23. Mastyło, M. (2013). Bilinear interpolation theorems and applications. Journal of Functional Analysis, 265(2), 185–207.

    Article  MathSciNet  Google Scholar 

  24. Murray, N., & Perronnin, F. (2014). Generalized max pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2473–2480).

  25. Naor, M., & Shamir, A. (1994). Visual cryptography. In Workshop on the theory and application of of cryptographic techniques (pp. 1–12). Springer.

  26. Rabin, T., & Ben-Or, M. (1989). Verifiable secret sharing and multiparty protocols with honest majority. In Proceedings of the twenty-first annual ACM symposium on theory of computing, STOC ’89 (pp. 73–85). New York, NY, USA: Association for Computing Machinery.

  27. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).

  28. Shamir, A. (1979). How to share a secret. Communications of the ACM, 22(11), 612–613.

    Article  MathSciNet  Google Scholar 

  29. Traore, B. B., Kamsu-Foguem, B., & Tangara, F. (2018). Deep convolution neural network for image recognition. Ecological Informatics, 48, 257–268.

    Article  Google Scholar 

  30. Verheul, E. R., & Van Tilborg, H. C. A. (1997). Constructions and properties of \(k\) out of \(n\) visual secret sharing schemes. Designs, Codes and Cryptography, 11(2), 179–196.

    Article  MathSciNet  Google Scholar 

  31. Zafar, A., Aamir, M., Nawi, N. M., Arshad, A., Riaz, S., Alruban, A., Dutta, A. K., & Almotairi, S. (2022). A comparison of pooling methods for convolutional neural networks. Applied Sciences, 12(17), 8643.

    Article  Google Scholar 

Download references

Acknowledgements

This research was (partially) funded by the Israeli Science Foundation (Grant No. 465/22) and by the Army Research Office under Grant Number W911NF-22-1-0225, and by Rita Altura trust chair in computer science. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Author information

Authors and Affiliations

  1. Computer Science Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Shlomi Dolev

  2. School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Alexander Fok & Michael Segal

Authors
  1. Shlomi Dolev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Fok
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Segal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Segal.

Ethics declarations

Conflict of interest

The authors do not have any conflict of interest in hiring, financial support, or others.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dolev, S., Fok, A. & Segal, M. Swarming with (visual) secret (shared) mission. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03840-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-024-03840-z

Keywords

Search

Navigation