Forgery Detection for Surveillance Video

  • Dai-Kyung Hyun
  • Min-Jeong Lee
  • Seung-Jin Ryu
  • Hae-Yeoun Lee
  • Heung-Kyu Lee
Conference paper

Abstract

In many courts, surveillance videos are used as important legal evidence. Nevertheless, little research is concerned with forgery of surveillance videos. In this paper, we present a forgery detection system for surveillance videos. We analyze the characteristic of surveillance videos. Subsequently, forgeries mainly occur to the surveillance videos are investigated. To identify both RGB and infrared video, Sensor Pattern Noise (SPN) for each video is transformed by Minimum Average Correlation Energy (MACE) filter. Manipulations on the given video are detected by estimating scaling factor and calculating correlation coefficient. Experimental results demonstrate that the proposed scheme is appropriate to identify forgeries of surveillance video.

Keywords

Sensor pattern noise Surveillance videos Forgery detection 

References

  1. 1.
    Chen, M., Fridrich, J., Miroslav Goljan, J.L.: Source digital camcorder identification using sensor photo response non-uniformity. In: The International Society for Optical Engineering (SPIE) (2007)Google Scholar
  2. 2.
    Gallagher, A.C.: Detection of linear and cubic interpolation in jpeg compressed images. In: Computer and Robot Vision (2005)Google Scholar
  3. 3.
    Goljan, M., Fridrich, J.: Camera identification from cropped and scaled images. In: The International Society for Optical Engineering (SPIE) (2008)Google Scholar
  4. 4.
    Hsu, C.C., Hung, T.Y., Lin, C.W., Hsu, C.T.: Video forgery detection using correlation of noise residue. In: IEEE 10th Workshop on Multimedia Signal Processing (2008)Google Scholar
  5. 5.
    Kerekes, R.A., Kumar, B.V.: Selecting a composite correlation filter design: a survey and comparative study. Optical Engineering 47(6) (2008)Google Scholar
  6. 6.
    Kobayashi, M., Okabe, T., Sato, Y.: Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans. Information Forensics and Security 5(4), 883–892 (2010)CrossRefGoogle Scholar
  7. 7.
    Kumar, B.V.K.V., Hassebrook, L.: Performance measures for correlation filters. Optical Society of America 29(20), 2997–3006 (1990)Google Scholar
  8. 8.
    Mahalanobis, A., Kumar, B.V.K.V., Casasent, D.: Minimum average correlation energy filters. Optical Society of America 26(17), 3633–3640 (1987)Google Scholar
  9. 9.
    Wang, W., Farid, H.: Exposing digital forgeries in video by detecting double mpeg compression. In: Multimedia and Security Workshop (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Dai-Kyung Hyun
    • 1
  • Min-Jeong Lee
    • 1
  • Seung-Jin Ryu
    • 1
  • Hae-Yeoun Lee
    • 2
  • Heung-Kyu Lee
    • 1
  1. 1.Department of CSKorea Advanced Institute of Science and Technology (KAIST)Yuseong-guRepublic of Korea
  2. 2.Department of Computer Software EngineeringKumoh National Institute of TechnologyGumiRepublic of Korea

Personalised recommendations