Skip to main content
SpringerLink
Log in

Detecting digital image forgery in near-infrared image of CCTV

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The reliability of CCTV digital images is more important than the reliability of many other types of images. However, image editing tools such as Photoshop make this unreliable. CCTV uses two photography modes, the RGB mode and the near-infrared mode. While near-infrared images have different properties, such as a constant level of source light intensity, and a constant direction of the source light, there are no forensic techniques for near-infrared images. In this paper, we propose a forensic technique based on a constant direction of the source light. In order to expose splicing forgery in near-infrared images, we create an ideal near-infrared image model of a plane. We then calculate gradient vectors of the model and objects in images. Depending on the similarity of two vectors, the image is determined forged or not. This forensic technique helps to improve the reliability of near-infrared images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

References

  1. Choi CH, Lee HY, Lee HK (2013) Estimation of color modification in digital images by cfa pattern change. Forensic Sci Int 226(1):94–105

    Article  Google Scholar 

  2. Dorsey, J., Rushmeier, H., Sillion, F.X. (2007): Digital modeling of material appearance. The Morgan Kaufmann series in computer graphics. Morgan Kaufmann

  3. Fridrich, A.J., Soukal, B.D., Lukas, A.J. (2003) Detection of copy-move forgery in digital images. In: in Proceedings of Digital Forensic Research Workshop. Citeseer

  4. Gill, M., Spriggs, A., Britain, G. (2005) Assessing the impact of CCTV. Home office research, development and statistics directorate London

  5. He J, Lin Z, Wang L, Tang X (2006) Detecting doctored jpeg images via dct coefficient analysis. Computer Vision–ECCV:423–435 Springer (2006)

  6. Hyun DK, Ryu SJ, Lee HY, Lee HK (2013) Detection of upscale-crop and partial manipulation in surveillance video based on sensor pattern noise. Sensors 13(9):12,605–12,631

    Article  Google Scholar 

  7. Johnson, M.K., Farid, H. (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th workshop on Multimedia and security, pp. 1–10. ACM

  8. Johnson MK, Farid H (2007a) Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forensics Secur 2(3):450–461

    Article  Google Scholar 

  9. Johnson, M.K., Farid, H. (2007b) Exposing digital forgeries through specular highlights on the eye. In: Information Hiding, pp. 311–325. Springer

  10. Kee, E., OBrien, J., Farid, H. (2014) Exposing photo manipulation from shading and shadows. ACM Trans. Graph. (in press 2014)

  11. Kirchner, M., Gloe, T. (2009) On resampling detection in re-compressed images. In: information forensics and security, 2009. WIFS 2009. First IEEE international workshop on, pp. 21–25. IEEE

  12. Li, B., Shi, Y.Q., Huang, J. (2008) Detecting doubly compressed jpeg images by using mode based first digit features. In: Multimedia Signal Processing, 2008 I.E. 10th Workshop on, pp. 730–735. IEEE

  13. Lukas, J., Fridrich, J., Goljan, M. (2006) Detecting digital image forgeries using sensor pattern noise. In: electronic imaging 2006, pp. 60,720Y–60,720Y. International Society for Optics and Photonics

  14. Piva, A. (2013) An overview on image forensics. ISRN Signal Process 2013

  15. Shi, Y.Q., Chen, C., Chen, W. (2007) A natural image model approach to splicing detection. In: Proceedings of the 9th workshop on Multimedia & security, pp. 51–62. ACM

  16. Stamm MC, Liu K (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5(3):492–506

    Article  Google Scholar 

  17. Stamm MC, Wu M, Liu K (2013) Information forensics: an overview of the first decade. Access, IEEE 1:167–200

    Article  Google Scholar 

  18. Taylor, A.E., et al. (2000) Illumination fundamentals. Lighting research center, Rensselaer Polytechnic Institute

  19. Yerushalmy I, Hel-Or H (2011) Digital image forgery detection based on lens and sensor aberration. Int J ComputVision 92(1):71–91

    MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. B0717-16-0135, Fundamental Research on Deep Learning based Complex Digital Image Forgery Detection).

Author information

Authors and Affiliations

  1. School of Computing, Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu, Daejeon, 34141, Republic of Korea

    Jin-Seok Park, Jong-Uk Hou, Do-Guk Kim & Heung-Kyu Lee

  2. Agency for Defense Development, Daejeon, Republic of Korea

    Dai-Kyung Hyun

Authors
  1. Jin-Seok Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dai-Kyung Hyun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jong-Uk Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Do-Guk Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Heung-Kyu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heung-Kyu Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, JS., Hyun, DK., Hou, JU. et al. Detecting digital image forgery in near-infrared image of CCTV. Multimed Tools Appl 76, 15817–15838 (2017). https://doi.org/10.1007/s11042-016-3871-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3871-7

Keywords

Search

Navigation