Advertisement

Satellite Image Forgery Detection Based on Buildings Shadows Analysis

  • Andrey Kuznetsov
  • Vladislav Myasnikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10716)

Abstract

Satellite images are to be effectively protected nowadays. There are a lot of ways of changing image content to hide important information: resampling, copy-move, object replacement and other attacks. When these changes are applied to satellite data inclination angles of shadows can be also changed. We propose a new method for satellite image forgery detection based on the analysis of high buildings shadows inclination angles on high resolution snapshots (0.5 m and less). In the proposed solution, the shadows are detected using Canny edge detector with further edge tracing. The comparison of both edge detection methods is presented in the experiments section. The next step is shadows inclination angles estimation using special model-oriented descriptors. The experiments show high accuracy of changed areas detection.

Keywords

Satellite image Forgery High buildings High resolution Shadow detection Inclination angle Canny edge detector Tracing 

References

  1. 1.
    Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopolou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  2. 2.
    Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 1(4), 154–160 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Farid, H.: Image forgery detection. IEEE Signal Process. Mag. 16–25 (2009)Google Scholar
  4. 4.
    Vladimirovich, K.A., Valerievich, M.V.: A fast plain copy-move detection algorithm based on structural pattern and 2D Rabin-karp rolling hash. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8814, pp. 461–468. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11758-4_50 Google Scholar
  5. 5.
    Canny, J.: A computational approach to edge detection. Pattern Anal. Mach. Intell. IEEE Trans. PAMI 8(6), 679–698 (1986)CrossRefGoogle Scholar
  6. 6.
    Ren, M., Yang, J., Sun, H.: Tracing boundary contours in a binary image. Image Vision Comput. 20(2), 125–131 (2002)CrossRefGoogle Scholar
  7. 7.
    Myasnikov, V.: Model-based gradient field descriptor as a convenient tool for image recognition and analysis. Comput. Opt. 36(4), 596–604 (2012)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Samara National Research UniversitySamaraRussia
  2. 2.Image Processing Systems Institute, Branch of the Federal Scientific Research Centre Crystallography and PhotonicsRussian Academy of SciencesSamaraRussia

Personalised recommendations