Advertisement

Your Privacy Is in Your Hand: Interactive Visual Privacy Control with Tags and Gestures

  • Jiayu ShuEmail author
  • Rui Zheng
  • Pan Hui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10340)

Abstract

Built–in cameras on mobile and wearable devices enable a number of vision related applications, such as mobile augmented reality, continuous sensing, and life-logging systems. While wearable cameras with smaller size and higher resolution bring joy and convenience to human lives, being recorded by unreliable cameras has raised people’s concerns about visual privacy, particularly the potential leak of identity. Consequently, protecting identity of people who are not willing to appear in the photo or video has become an urgent issue that has yet to be resolved. In this paper, we propose an interactive method for individuals to control their visual privacy based on privacy indicators and control rules. We design and implement a prototype on Android smartphones, which allows individuals to inform cameras of their privacy control intentions through interaction using tags, hand gestures, and their combinations. Protection measures such as blurring the face will be performed to remove individual’s identifiable information according to control rules. Evaluation results of the overall performance and feedbacks from real users demonstrate the effectiveness and usability of our approach, showing potentials of interactive visual privacy control.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Acquisti, A., Gross, R., Stutzman, F.: Privacy in the age of augmented reality. In: Proceedings of the National Academy of Sciences (2011)Google Scholar
  5. 5.
    Aditya, P., Sen, R., Druschel, P., Oh, S.J., Benenson, R., Fritz, M., Schiele, B., Bhattacharjee, B., Wu, T.T.: I-Pic: a platform for privacy-compliant image capture. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys, vol. 16 (2016)Google Scholar
  6. 6.
    Bardyn, J., et al.: Une architecture Vlsi pour un operateur de filtrage median. In: Congres Reconnaissance des Formes et Intelligence Artificielle, vol. 1, pp. 557–566 (1984)Google Scholar
  7. 7.
    Bay, H., Tuytelaars, T., Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi: 10.1007/11744023_32 CrossRefGoogle Scholar
  8. 8.
    Bo, C., Shen, G., Liu, J., Li, X.Y., Zhang, Y., Zhao, F.: Privacy.tag: privacy concern expressed and respected. In: Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 163–176. ACM (2014)Google Scholar
  9. 9.
    Caine, K.E., Fisk, A.D., Rogers, W.A.: Benefits and privacy concerns of a home equipped with a visual sensing system: a perspective from older adults. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, pp. 180–184. Sage Publications (2006)Google Scholar
  10. 10.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989)CrossRefGoogle Scholar
  11. 11.
    Denning, T., Dehlawi, Z., Kohno, T.: In situ with bystanders of augmented reality glasses: perspectives on recording and privacy-mediating technologies. In: Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 2377–2386. ACM (2014)Google Scholar
  12. 12.
    Fiala, M.: Artag, a fiducial marker system using digital techniques. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 590–596. IEEE (2005)Google Scholar
  13. 13.
    Gavison, R.: Privacy and the limits of law. Yale Law J. 89, 421–471 (1980)CrossRefGoogle Scholar
  14. 14.
    Hoyle, R., Templeman, R., Anthony, D., Crandall, D., Kapadia, A.: Sensitive lifelogs: a privacy analysis of photos from wearable cameras. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1645–1648. ACM (2015)Google Scholar
  15. 15.
    Hoyle, R., Templeman, R., Armes, S., Anthony, D., Crandall, D., Kapadia, A.: Privacy behaviors of lifeloggers using wearable cameras. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 571–582. ACM (2014)Google Scholar
  16. 16.
    Jana, S., Molnar, D., Moshchuk, A., Dunn, A., Livshits, B., Wang, H.J., Ofek, E.: Enabling fine-grained permissions for augmented reality applications with recognizers. In: Presented as part of the 22nd USENIX Security Symposium (USENIX Security 2013), pp. 415–430 (2013)Google Scholar
  17. 17.
    Jana, S., Narayanan, A., Shmatikov, V.: A scanner darkly: protecting user privacy from perceptual applications. In: 2013 IEEE Symposium on Security and Privacy (SP), pp. 349–363. IEEE (2013)Google Scholar
  18. 18.
    Kärkkäinen, T., Vaittinen, T., Väänänen-Vainio-Mattila, K.: I don’t mind being logged, but want to remain in control: a field study of mobile activity and context logging. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 163–172. ACM (2010)Google Scholar
  19. 19.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  20. 20.
    Nguyen, D.H., Bedford, A., Bretana, A.G., Hayes, G.R.: Situating the concern for information privacy through an empirical study of responses to video recording. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3207–3216. ACM (2011)Google Scholar
  21. 21.
    Raval, N., Srivastava, A., Razeen, A., Lebeck, K., Machanavajjhala, A., Cox, L.P.: What you mark is what apps see. In: ACM International Conference on Mobile Systems, Applications, and Services (Mobisys) (2016)Google Scholar
  22. 22.
    Roesner, F., Kohno, T., Molnar, D.: Security and privacy for augmented reality systems. Commun. ACM 57(4), 88–96 (2014)CrossRefGoogle Scholar
  23. 23.
    Roesner, F., Molnar, D., Moshchuk, A., Kohno, T., Wang, H.J.: World-driven access control for continuous sensing. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1169–1181. ACM (2014)Google Scholar
  24. 24.
    Schiff, J., Meingast, M., Mulligan, D.K., Sastry, S., Goldberg, K.: Respectful cameras: detecting visual markers in real-time to address privacy concerns. In: Senior, A. (ed.) Protecting Privacy in Video Surveillance, pp. 65–89. Springer, London (2009)CrossRefGoogle Scholar
  25. 25.
    Shaw, R.: Recognition markets and visual privacy. In: UnBlinking: New Perspectives on Visual Privacy in the 21st Century (2006)Google Scholar
  26. 26.
    Shu, J., Zheng, R., Hui, P.: Demo: interactive visual privacy control with gestures. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion, p. 120. ACM (2016)Google Scholar
  27. 27.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  28. 28.
    Wachs, J., Stern, H., Edan, Y., Gillam, M., Feied, C., Smith, M., Handler, J.: A real-time hand gesture interface for medical visualization applications. In: Knowles, J., Avineri, E., Dahal, K. (eds.) Applications of Soft Computing, pp. 153–162. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  29. 29.
    Zhang, X., Fronz, S., Navab, N.: Visual marker detection and decoding in AR systems: a comparative study. In: Proceedings of the 1st International Symposium on Mixed and Augmented Reality, p. 97. IEEE Computer Society (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Hong Kong University of Science and TechnologyKowloonHong Kong

Personalised recommendations