Visual Cryptography for Detecting Hidden Targets by Small-Scale Robots

  • Danilo Avola
  • Luigi Cinque
  • Gian Luca Foresti
  • Daniele Pannone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11351)


The last few years have seen a growing use of robots to replace humans in dangerous activities, such as inspections, border control, and military operations. In some application areas, as the latter, there is the need to hide strategic information, such as acquired data or relevant positions. This paper presents a vision based system to find encrypted targets in unknown environments by using small-scale robots and visual cryptography. The robots acquire a scene by a standard RGB camera and use a visual cryptography based technique to encrypt the data. The latter is subsequently sent to a server whose purpose is to decrypt and analyse it for searching target objects or tactic positions. To show the effectiveness of the proposed system, the experiments were performed by using two robots, i.e., a small-scale rover in indoor environments and a small-scale Unmanned Aerial Vehicle (UAV) in outdoor environments. Since the current literature does not contain other approaches comparable with that we propose, the obtained remarkable results and the proposed method can be considered as baseline in the area of encrypted target search by small-scale robots.


Visual cryptography Encrypted target Shares generation Target recognition Rover UAV RGB camera SLAM 


  1. 1.
    Cacace, J., Finzi, A., Lippiello, V., Furci, M., Mimmo, N., Marconi, L.: A control architecture for multiple drones operated via multimodal interaction in search rescue mission. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 233–239 (2016)Google Scholar
  2. 2.
    Kiyani, M.N., Khan, M.U.M.: A prototype of search and rescue robot. In: International Conference on Robotics and Artificial Intelligence, pp. 208–213 (2016)Google Scholar
  3. 3.
    Avola, D., Foresti, G.L., Martinel, N., Micheloni, C., Pannone, D., Piciarelli, C.: Real-time incremental and geo-referenced mosaicking by small-scale UAVs. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 694–705. Springer, Cham (2017). Scholar
  4. 4.
    Avola, D., Foresti, G.L., Martinel, N., Micheloni, C., Pannone, D., Piciarelli, C.: Aerial video surveillance system for small-scale UAV environment monitoring. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 1–6 (2017)Google Scholar
  5. 5.
    Avola, D., Cinque, L., Foresti, G.L., Martinel, N., Pannone, D., Piciarelli, C.: A UAV video dataset for mosaicking and change detection from low-altitude flights. IEEE Trans. Syst. Man Cybern. Syst. PP, 1–11 (2018)CrossRefGoogle Scholar
  6. 6.
    Kaur, T., Kumar, D.: Wireless multifunctional robot for military applications. In: International Conference on Recent Advances in Engineering Computational Sciences, pp. 1–5 (2015)Google Scholar
  7. 7.
    Kopuletý, M., Palasiewicz, T.: Advanced military robots supporting engineer reconnaissance in military operations. In: Mazal, J. (ed.) MESAS 2017. LNCS, vol. 10756, pp. 285–302. Springer, Cham (2018). Scholar
  8. 8.
    Avola, D., Cinque, L., Foresti, G.L., Marini, M.R., Pannone, D.: A rover-based system for searching encrypted targets in unknown environments. In: International Conference on Pattern Recognition Applications and Methods, vol. 1, pp. 254–261 (2018)Google Scholar
  9. 9.
    Avola, D., Foresti, G.L., Cinque, L., Massaroni, C., Vitale, G., Lombardi, L.: A multipurpose autonomous robot for target recognition in unknown environments. In: IEEE International Conference on Industrial Informatics, pp. 766–771 (2016)Google Scholar
  10. 10.
    Naor, M., Shamir, A.: Visual cryptography. In: De Santis, A. (ed.) EUROCRYPT 1994. LNCS, vol. 950, pp. 1–12. Springer, Heidelberg (1995). Scholar
  11. 11.
    Liu, S., Fujiyoshi, M., Kiya, H.: A cheat preventing method with efficient pixel expansion for Naor-Shamir’s visual cryptography. In: IEEE International Conference on Image Processing, pp. 5527–5531 (2014)Google Scholar
  12. 12.
    Li, P., Yang, C.N., Kong, Q.: A novel two-in-one image secret sharing scheme based on perfect black visual cryptography. J. R. Time Image Process. 14, 41–50 (2018)CrossRefGoogle Scholar
  13. 13.
    Shivani, S.: VMVC: verifiable multi-tone visual cryptography. Multimed. Tools Appl. 77, 5169–5188 (2018)CrossRefGoogle Scholar
  14. 14.
    Alex, N.S., Anbarasi, L.J.: Enhanced image secret sharing via error diffusion in halftone visual cryptography. In: International Conference on Electronics Computer Technology, pp. 393–397 (2011)Google Scholar
  15. 15.
    Pahuja, S., Kasana, S.S.: Halftone visual cryptography for color images. In: International Conference on Computer, Communications and Electronics, pp. 281–285 (2017)Google Scholar
  16. 16.
    Lin, C.C., Tsai, W.H.: Visual cryptography for gray-level images by dithering techniques. Pattern Recognit. Lett. 24, 349–358 (2003)CrossRefGoogle Scholar
  17. 17.
    Babu, R., Sridhar, M., Babu, B.R.: Information hiding in gray scale images using pseudo-randomized visual cryptography algorithm for visual information security. In: International Conference on Information Systems and Computer Networks, pp. 195–199 (2013)Google Scholar
  18. 18.
    Hou, Y.C.: Visual cryptography for color images. Pattern Recognit. 36, 1619–1629 (2003)CrossRefGoogle Scholar
  19. 19.
    Stinson, D.R.: An introduction to visual cryptography. In: Public Key Solutions, pp. 28–30 (1997)Google Scholar
  20. 20.
    Shyu, S.J.: Efficient visual secret sharing scheme for color images. Pattern Recognit. 39, 866–880 (2006)CrossRefGoogle Scholar
  21. 21.
    Yang, D., Doh, I., Chae, K.: Enhanced password processing scheme based on visual cryptography and OCR. In: International Conference on Information Networking, pp. 254–258 (2017)Google Scholar
  22. 22.
    Kadhim, A., Mohamed, R.M.: Visual cryptography for image depend on RSA algamal algorithms. In: Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications, pp. 1–6 (2016)Google Scholar
  23. 23.
    Joseph, S.K., Ramesh, R.: Random grid based visual cryptography using a common share. In: International Conference on Computing and Network Communications, pp. 656–662 (2015)Google Scholar
  24. 24.
    Leonard, J.J., Durrant-Whyte, H.F.: Simultaneous map building and localization for an autonomous mobile robot. In: IEEE/RSJ International Workshop on Intelligent Robots and Systems, Intelligence for Mechanical Systems, pp. 1442–1447 (1991)Google Scholar
  25. 25.
    Sim, R., Roy, N.: Global a-optimal robot exploration in slam. In: IEEE International Conference on Robotics and Automation, pp. 661–666 (2005)Google Scholar
  26. 26.
    Trivun, D., Šalaka, E., Osmanković, D., Velagić, J., Osmić, N.: Active SLAM-based algorithm for autonomous exploration with mobile robot. In: IEEE International Conference on Industrial Technology, pp. 74–79 (2015)Google Scholar
  27. 27.
    Li, C., Wei, H., Lan, T.: Research and implementation of 3D SLAM algorithm based on kinect depth sensor. In: International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 1070–1074 (2016)Google Scholar
  28. 28.
    Walas, K., Nowicki, M., Ferstl, D., Skrzypczyński, P.: Depth data fusion for simultaneous localization and mapping - RGB-DD SLAM. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 9–14 (2016)Google Scholar
  29. 29.
    Chen, L., Sun, L., Yang, T., Fan, L., Huang, K., Xuanyuan, Z.: RGB-T SLAM: a flexible slam framework by combining appearance and thermal information. In: IEEE International Conference on Robotics and Automation, pp. 5682–5687 (2017)Google Scholar
  30. 30.
    Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33, 1255–1262 (2017)CrossRefGoogle Scholar
  31. 31.
    Camurri, M., Bazeille, S., Caldwell, D.G., Semini, C.: Real-time depth and inertial fusion for local SLAM on dynamic legged robots. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 259–264 (2015)Google Scholar
  32. 32.
    Bu, S., Zhao, Y., Wan, G., Liu, Z.: Map2DFusion: real-time incremental UAV image mosaicing based on monocular slam. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4564–4571 (2016)Google Scholar
  33. 33.
    Kim, D.Y., Kim, J., Kim, I., Jun, S.: Artificial landmark for vision-based slam of water pipe rehabilitation robot. In: International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 444–446 (2015)Google Scholar
  34. 34.
    Balcilar, M., Yavuz, S., Amasyali, M.F., Uslu, E., Çakmak, F.: R-slam: resilient localization and mapping in challenging environments. Robot. Auton. Syst. 87, 66–80 (2017)CrossRefGoogle Scholar
  35. 35.
    Boiangiu, C.A., Bucur, I., Tigora, A.: The image binarization problem revisited: perspectives and approaches. J. Inf. Syst. Oper. Manag. 6, 1–10 (2012)Google Scholar
  36. 36.
    Knuth, D.E.: Digital halftones by dot diffusion. ACM Trans. Graph. 6, 245–273 (1987)CrossRefGoogle Scholar
  37. 37.
    Bayer, B.E.: An optimum method for two-level rendition of continuous-tone pictures. In: IEEE International Conference on Communications, vol. 26, pp. 11–15 (1973)Google Scholar
  38. 38.
    Jarvis, J.F., Judice, C.N., Ninke, W.: A survey of techniques for the display of continuous tone pictures on bilevel displays. Comput. Graph. Image Process. 5, 13–40 (1976)CrossRefGoogle Scholar
  39. 39.
    Avola, D., Bernardi, M., Cinque, L., Foresti, G.L., Massaroni, C.: Adaptive bootstrapping management by keypoint clustering for background initialization. Pattern Recognit. Lett. 100, 110–116 (2017)CrossRefGoogle Scholar
  40. 40.
    Avola, D., Cinque, L., Foresti, G.L., Massaroni, C., Pannone, D.: A keypoint-based method for background modeling and foreground detection using a PTZ camera. Pattern Recognit. Lett. 96, 96–105 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Danilo Avola
    • 1
  • Luigi Cinque
    • 2
  • Gian Luca Foresti
    • 1
  • Daniele Pannone
    • 2
  1. 1.Department of Mathematics, Computer Science and PhysicsUniversity of UdineUdineItaly
  2. 2.Department of Computer ScienceSapienza UniversityRomeItaly

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