Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 14, pp 19839–19860 | Cite as

Estimation of lighting environment for exposing image splicing forgeries

  • Aniruddha MazumdarEmail author
  • Prabin Kumar Bora
Article
  • 115 Downloads

Abstract

This paper proposes a novel image forensics technique to detect splicing forgeries in digital images. The method is applicable to images containing two or more persons, where the near frontal views of the faces are available. Firstly, a low-dimensional lighting model is created from a set of front pose face images of a single individual under different directional lighting environments. For this, the set of images is decomposed using principal component analysis. This low-dimensional model, which captures the lighting variation in faces, is then used to estimate the lighting environment (LE) from a given near front pose face image. In a spliced image, the LE estimated from the spliced face will be different from that estimated from the original faces. Therefore, finding the inconsistencies among the LEs estimated from different faces could reveal the splicing forgery. The experimental results on Yale Face Database B and a set of authentic and forged real-life images show the efficacy of the proposed method.

Keywords

Image forensics Lighting estimation PCA 

Notes

References

  1. 1.
    Basri R, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25:218–233CrossRefGoogle Scholar
  2. 2.
    Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720CrossRefGoogle Scholar
  3. 3.
    Belhumeur P, Kriegman D (1998) What is the set of images of an object under all possible illumination conditions. Int J Comput Vis 28:245–260CrossRefGoogle Scholar
  4. 4.
    Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhencement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9:515–525CrossRefGoogle Scholar
  5. 5.
    Carvalho T, Faria FA, Pedrini H, Torres RS, Rocha A (2016) Illuminant-based transformed spaces for image forensics. IEEE Trans Inf Forensics Secur 11:720–733CrossRefGoogle Scholar
  6. 6.
    Carvalho T, Riess C, Angelopoulou E, Pedrini H, Rocha A (2013) Exposing digital image forgeries by illumination color classification. IEEE Trans Inf Forensics Secur 8:1182–1194CrossRefGoogle Scholar
  7. 7.
    Epstein R, Hallinan PW, Yuille AL (1995) 5 ± 2 eigenimages suffice: an empirical investigation of low-dimensional lighting models. In: Proc IEEE workshop on physics-based vision, pp 108–116Google Scholar
  8. 8.
    Farid H (2009) Exposing digital forgeries from jpeg ghosts. IEEE Trans Inf Forensics Secur 4:154–160CrossRefGoogle Scholar
  9. 9.
    Geoghiades A, Belhumeur P, Kriegman D (2001) From Few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23:643–660CrossRefGoogle Scholar
  10. 10.
    Gholap S, Bora PK (2008) Illuminant colour based image forensics. In: Proc IEEE region 10 Conf, vol 17, pp 1–5Google Scholar
  11. 11.
    Hallinan PW (1994) A low-dimensional representation of human faces for arbitrary lighting conditions. In: Proc IEEE computer society conference on computer vision and pattern recognition, pp 995–999Google Scholar
  12. 12.
    Huang R, Smith WAP (2011) Shape-from-shading under complex natural illumination. In: Proc of IEEE Int Conf on image process, pp 13–16Google Scholar
  13. 13.
    Johnson MK, Farid H (2005) Exposing digital image forgeries by detecting inconsistencies in lighting. In: ACM multimedia and security workshopGoogle Scholar
  14. 14.
    Johnson MK, Farid H (2007) Exposing digital forgeries through specular highlights on the eye. In: Int workshop on inform hiding, pp 311–325Google Scholar
  15. 15.
    Johnson MK, Farid H (2007) Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forensics Secur 3:450–461CrossRefGoogle Scholar
  16. 16.
    Kee E, Farid H (2010) Exposing digital forgeries from 3-d lighting environments. In: IEEE Int workshop on information forensics and security (WIFS), pp 1–6Google Scholar
  17. 17.
    Lee K-C, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27:684–698CrossRefGoogle Scholar
  18. 18.
    Liu Q (2017) An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics. Pattern Recogn 65:35–46CrossRefGoogle Scholar
  19. 19.
    Mayer O, Stamm MC (2018) Accurate and efficient image forgery detection using lateral chromatic aberration. IEEE transactions on information forensics and securityGoogle Scholar
  20. 20.
    Mazumdar A, Bora PK (2016) Exposing splicing forgeries in digital images through dichromatic plane histogram discrepancies. In: Proc Indian conference on computer vision, graphics and image processingGoogle Scholar
  21. 21.
    Murase H, Nayar S (1995) Visual learning and recognition of 3-d objects from appearance. Int J Comput Vis 14:5–24CrossRefGoogle Scholar
  22. 22.
    Peng B, Wang W, Dong J, Tan T (2017) Optimized 3d lighting environment estimation for image forgery detection. IEEE Trans Inf Forensics Secur 12:479–494CrossRefGoogle Scholar
  23. 23.
    Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53:3948–3959MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Ramamoorthy R (2002) Analytic pca construction for theoretical analysis of lighting variability in images of lambertian object. IEEE Trans Pattern Anal Mach Intell 24:1322–1333CrossRefGoogle Scholar
  25. 25.
    Ramamoorthy R, Hanrahan P (2001) On the relationship between radiance and irradiance: Determining the illumination from images of a convex lambertian object. J Opt Soc Amer A. 18:2448–2559MathSciNetCrossRefGoogle Scholar
  26. 26.
    Riess C, Pfaller S, Angelopoulou E (2015) Reflectance normalization in illumination-based image manipulation detection. New trends in Image Analysis and Processing, pp 3–10Google Scholar
  27. 27.
    Saboia P, Carvalho T, Rocha A (2011) Eye specular highlights telltales for digital forensics: a machine learning approach. In: IEEE Int Conf image processing (ICIP), pp 1937–1940Google Scholar
  28. 28.
    Shashua A (1992) Geometry and photometry in 3d visual recognition. PhD dissertation, Massachusetts Institute of TechnologyGoogle Scholar
  29. 29.
    Shashua A (1997) On photometric issues in 3d visual recognition from a single 2d image. Int J Comput Vis 21:99–122CrossRefGoogle Scholar
  30. 30.
    Wang P, Liu F, Yang C, Luo X (2018) Blind forensics of image gamma transformation and its application in splicing detection. J Vis Commun Image Represent 55:80–90CrossRefGoogle Scholar
  31. 31.
    Wang P, Liu F, Yang C, Luo X (2018) Parameter estimation of image gamma transformation based on zero-value histogram bin locations. Signal Process Image Commun 64:33–45CrossRefGoogle Scholar
  32. 32.
    Wei F, kai W, Francois C, Zhang X (2012) 3d lighting-based image forgery detection using shape-from-shading. In: Proc 20th Eur Conf signal process Conf (EUSIPCO), pp 1777–1781Google Scholar
  33. 33.
    Yuille A, Snow D, Epstein R, Belhumeur P (1999) Determining generative models of object under varying illumination: shape and albedo from multiple images using svd and integrability. Int J Comput Vis 21:99–122Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiAssamIndia

Personalised recommendations