CCF Chinese Conference on Computer Vision

Computer Vision pp 325-334 | Cite as

An Image Forensic Technique Based on 2D Lighting Estimation Using Spherical Harmonic Frames

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 546)

Abstract

In this paper, a novel approach for exposing digital image tampering based on the theory of spherical harmonic frames is presented. We describe a robust technique for exposing digital forgeries that we utilize the information along a 2D occluding contour and estimate the lighting feature using spherical harmonic frames. Spherical harmonic frames are generated by the rotation along the symmetry axes of a symmetry group. The lighting-based digital forensic technique using spherical harmonic frames inherits the robust property of frames and improve the statistical results compared with spherical harmonic bases. Experimental results performed using spherical harmonic frames prove the robust measurements and discriminability of the complex lighting environments from synthetic data and real data. The application of identifying the tampered images reveals the improvement of our method.

Keywords

Spherical harmonic frames Digital forensic technique Lighting 

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References

  1. 1.
    Farid, H.: A Survey of Image Forgery Detection. IEEE Signal Processing Magazine 26, 16–25 (2009)CrossRefGoogle Scholar
  2. 2.
    Fridrich, J.: Digital Image Forensics. IEEE Signal Processing Magazine 26, 26–37 (2009)CrossRefGoogle Scholar
  3. 3.
    Swaminathan, A., Wu, M., Liu, K.J.R.: Component Forensics: Theory, methodologies, and applications. IEEE Signal Processing Magazine 26, 38–48 (2009)CrossRefGoogle Scholar
  4. 4.
    Ng, T.T., Chang, S.F.: Identifying and Prefiltering Images: Distinguishing between natural photography and photorealistic computer graphics. IEEE Signal Processing Magazine 26, 49–58 (2009)CrossRefGoogle Scholar
  5. 5.
    Rocha, A., Scheirer, W., Boult, T.E., Goldenstein, S.: Vision of the unseen: Current trends and challenges in digital image and video forensics. ACM Computing Surveys (CSUR) 43, 26:1–26:42 (2011)CrossRefGoogle Scholar
  6. 6.
    Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th Workshop on Multimedia and Security, pp. 1–10. ACM (2005)Google Scholar
  7. 7.
    Johnson, M.K., Farid, H.: Exposing digital forgeries through specular highlights on the eye. In: 9th International Workshop on Information Hiding, pp. 311–325 (2007)Google Scholar
  8. 8.
    Johnson, M.K., Farid, H.: Exposing Digital Forgeries in Complex Lighting Environments. IEEE Transactions on Information Forensics and Security 2, 450–461 (2007)CrossRefGoogle Scholar
  9. 9.
    Stork, D.G., Johnson, M.K.: Lighting analysis of diffusely illuminated tableaus in realist paintings: an application to detecting ‘compositing’ in the portraits of Garth Herrick. In: Electronic Imaging: Media Forensics and Security, pp. 72540L1-8. SPIE (2009)Google Scholar
  10. 10.
    Farid, H., Kee, E.: Exposing digital forgeries from 3-D lighting environments. In: IEEE International Workshop on Information Forensics and Security (2010)Google Scholar
  11. 11.
    Zhao, W.Y., Chen, S.L., Zheng, Y., Chen, S.L., Peng, S.L.: Lighting Estimation of a Convex Lambertian Object Using Redundant Spherical Harmonic Frames. Journal of Computer Science and Technology 28, 454–467 (2013)CrossRefMATHGoogle Scholar
  12. 12.
    Nillius, P., Eklundh, J.O.: Automatic estimation of the projected light source direction. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1076–1083. IEEE (2001)Google Scholar
  13. 13.
    Brunelli, R., Messelodi, S.: Robust estimation of correlation with applications to computer vision. Pattern Recognition, 833–841 (1995)Google Scholar
  14. 14.
    Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environment maps. In: Proceeding SIGGRAPH 2001 Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 497–500. ACM (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Engineering Lab on Intelligent Perception for Internet of Things (ELIP), Shenzhen Graduate SchoolPeking UniversityBeijingChina

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