Remote Sensing Data Copy-Move Forgery Protection Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9972)

Abstract

Copy-move attack is one of the most popular digital image forgery attacks. The main problem is that existing studies do not provide high detection accuracy with low computational complexity. High complexity of existing feature based solutions makes impossible to use them for large remote sensing snapshots analysis. In this paper there is proposed a copy-move detection algorithm based on perceptual hash value calculation. Hash values are evaluated using the result of binary gradient contours computation. The proposed solution showed high detection accuracy and low computational complexity for copy-move detection in remote sensing data.

References

  1. 1.
    Christlein, V., Riess, C., Jordan, J., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  2. 2.
    Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)CrossRefGoogle Scholar
  3. 3.
    Bayram, S., Sencar, H., Memon, H.: An efficient and robust method for detecting copy-move forgery. IEEE Int. Conf. Acoust. Speech Signal Process. 2009, 1053–1056 (2009)Google Scholar
  4. 4.
    Glumov, N., Kuznetsov, A., Myasnikov, V.: Algorithms for detection of plain copy-move regions in digital images. Pattern Recogn. Image Anal. 25(3), 423–429 (2015)CrossRefGoogle Scholar
  5. 5.
    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, Heidelberg (2014). doi:10.1007/978-3-319-11758-4_50 Google Scholar
  6. 6.
    Davarzani, R., Yaghmaie, K., Mozaffari, S., Tapak, M.: Copy-move forgery detection using multi-resolution local binary patterns. Forensic Sci. Int. 231(1–3), 61–72 (2013)CrossRefGoogle Scholar
  7. 7.
    Fernndez, A., lvarez, M.X., Bianconi, F.: Image classification with binary gradient contours. Opt. Lasers Eng. 49(910), 1177–1184 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Samara National Research UniversitySamaraRussia

Personalised recommendations