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Estimation of lighting environment for exposing image splicing forgeries

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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.

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Notes

  1. These images are also analysed in [5].

References

  1. Basri R, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25:218–233

    Article  Google Scholar 

  2. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720

    Article  Google Scholar 

  3. Belhumeur P, Kriegman D (1998) What is the set of images of an object under all possible illumination conditions. Int J Comput Vis 28:245–260

    Article  Google Scholar 

  4. Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhencement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9:515–525

    Article  Google Scholar 

  5. Carvalho T, Faria FA, Pedrini H, Torres RS, Rocha A (2016) Illuminant-based transformed spaces for image forensics. IEEE Trans Inf Forensics Secur 11:720–733

    Article  Google Scholar 

  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 8:1182–1194

    Article  Google Scholar 

  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 108–116

  8. Farid H (2009) Exposing digital forgeries from jpeg ghosts. IEEE Trans Inf Forensics Secur 4:154–160

    Article  Google Scholar 

  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 23:643–660

    Article  Google Scholar 

  10. Gholap S, Bora PK (2008) Illuminant colour based image forensics. In: Proc IEEE region 10 Conf, vol 17, pp 1–5

  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 995–999

  12. Huang R, Smith WAP (2011) Shape-from-shading under complex natural illumination. In: Proc of IEEE Int Conf on image process, pp 13–16

  13. Johnson MK, Farid H (2005) Exposing digital image forgeries by detecting inconsistencies in lighting. In: ACM multimedia and security workshop

  14. Johnson MK, Farid H (2007) Exposing digital forgeries through specular highlights on the eye. In: Int workshop on inform hiding, pp 311–325

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

    Article  Google Scholar 

  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 1–6

  17. Lee K-C, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27:684–698

    Article  Google Scholar 

  18. Liu Q (2017) An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics. Pattern Recogn 65:35–46

    Article  Google Scholar 

  19. Mayer O, Stamm MC (2018) Accurate and efficient image forgery detection using lateral chromatic aberration. IEEE transactions on information forensics and security

  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 processing

  21. Murase H, Nayar S (1995) Visual learning and recognition of 3-d objects from appearance. Int J Comput Vis 14:5–24

    Article  Google Scholar 

  22. Peng B, Wang W, Dong J, Tan T (2017) Optimized 3d lighting environment estimation for image forgery detection. IEEE Trans Inf Forensics Secur 12:479–494

    Article  Google Scholar 

  23. Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53:3948–3959

    Article  MathSciNet  MATH  Google Scholar 

  24. Ramamoorthy R (2002) Analytic pca construction for theoretical analysis of lighting variability in images of lambertian object. IEEE Trans Pattern Anal Mach Intell 24:1322–1333

    Article  Google Scholar 

  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. 18:2448–2559

    Article  MathSciNet  Google Scholar 

  26. Riess C, Pfaller S, Angelopoulou E (2015) Reflectance normalization in illumination-based image manipulation detection. New trends in Image Analysis and Processing, pp 3–10

  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 1937–1940

  28. Shashua A (1992) Geometry and photometry in 3d visual recognition. PhD dissertation, Massachusetts Institute of Technology

  29. Shashua A (1997) On photometric issues in 3d visual recognition from a single 2d image. Int J Comput Vis 21:99–122

    Article  Google Scholar 

  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 55:80–90

    Article  Google Scholar 

  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 64:33–45

    Article  Google Scholar 

  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 1777–1781

  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 21:99–122

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Assam, 781039, India

    Aniruddha Mazumdar & Prabin Kumar Bora

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  1. Aniruddha Mazumdar
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  2. Prabin Kumar Bora
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Correspondence to Aniruddha Mazumdar.

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Mazumdar, A., Bora, P.K. Estimation of lighting environment for exposing image splicing forgeries. Multimed Tools Appl 78, 19839–19860 (2019). https://doi.org/10.1007/s11042-018-7147-2

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  • DOI: https://doi.org/10.1007/s11042-018-7147-2

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