Skip to main content
SpringerLink
Log in

Detecting Doctored Images

  • Chapter
  • First Online:
Digital Image Forensics

  • 3213 Accesses

Abstract

With the availability of powerful image-editing software, digital cameras, and a wealth of online imagery, almost anyone can doctor an image. Consequently, image hoaxes are now commonplace and people even expect that images of celebrities have been retouched. For many, seeing is no longer believing. While most doctored images are made for entertainment or artistic purposes, they have also shown up in courtrooms as evidence and in scientific publications. The presence of manipulated images in these settings is troubling and the field of digital image forensics has emerged to address this growing problem. This chapter provides an overview of current tools for detecting doctored images and discusses trends that have emerged in the field. Several tools are described in detail and references are provided for related techniques.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    With four points, the rank of matrix \(M\) is 8, one less than the number of unknowns in \(\mathbf{ h} \). This is sufficient as we only can recover \(\mathbf{ h} \) to within a scale factor.

References

  1. Avcibas I, Bayram S, Memon N, Sankur B, Ramkumar M (2004) A classifier design for detecting image manipulations. In: 2004 International Conference on Image Processing, ICIP ’04, vol 4. pp 2645–2648, 2004

    Google Scholar 

  2. Ronen B, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233

    Google Scholar 

  3. Blanz V, Vetter T (1999) A morphable model for the synthesis of 3D faces. In: Proceedings of the SIGGRAPH, pp 187–194, 1999

    Google Scholar 

  4. Chen M, Fridrich J, Goljan M, Lukáš J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensics Secur 3(1):74–90

    Article  Google Scholar 

  5. Chojnacki W, Brooks MJ (1994) Revisiting Pentland’s estimator of light source direction. J Opt Soc Am 11(1):118–124

    Article  Google Scholar 

  6. Conotter V, Boato G, Farid H (2010) Detecting photo manipulations on signs and billboards. In: International Conference on Image Processing, pp 1–4, 2010

    Google Scholar 

  7. Criminisi A, Reid I, Zisserman A (2000) Single view metrology. Int J Comput Vis 40(2):123–148

    Article  MATH  Google Scholar 

  8. Debevec PE, Malik J (1997) Recovering high dynamic range radiance maps from photographs. In: Proceedings of the 24th annual conference on Computer graphics and interactive techniques (SIGGRAPH), 1997

    Google Scholar 

  9. Dirik AE, Sencar HT, Memon N (2008) Digital single lens reflex camera identification from traces of sensor dust. IEEE Trans Inf Forensics Secur, 3(3):539–552

    Google Scholar 

  10. Farid H (2006) Digital image ballistics from JPEG quantization. Technical Report TR2006-583, Department of Computer Science, Dartmouth College, 2006

    Google Scholar 

  11. Farid H (2008) Digital ballistics from JPEG quantization: a followup study. Technical Report TR2008-638, Department of Computer Science, Dartmouth College, 2008

    Google Scholar 

  12. Farid H, Bravo MJ (2010) Image forensic analyses that elude the human visual system. In: SPIE Symposium on Electronic, Imaging, pp 1–10, 2010

    Google Scholar 

  13. Faugeras O (1993) Three-Dimensional Computer Vision. MIT Press, Cambridge

    Google Scholar 

  14. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  15. Jessica F, David S, Lukáš J (2003) Detection of copy-move forgery in digital images, In: Proceedings of DFRWS, 2003

    Google Scholar 

  16. Goldman DB (2010) Vignette and exposure calibration and compensation. IEEE Trans Pattern Anal Mach Intell, 32(12):2276–2288

    Google Scholar 

  17. Goljan M, Chen M, Fridrich J (2007) Identifying common source digital camera from image pairs. In: Proceedings of the IEEE ICIP, 2007

    Google Scholar 

  18. Goljan M, Fridrich J (2008) Camera identification from cropped and scaled images. In: Proceedings of the SPIE Electronic Imaging: Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, vol 6819. 2008

    Google Scholar 

  19. Grossberg MD, Nayar SK (2004) Modeling the space of camera response functions. IEEE Trans Pattern Anal Mach Intell 26(10):1272–1282

    Article  Google Scholar 

  20. Hartley R, Zisserman A (2004) Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge

    Google Scholar 

  21. Horn BKP (1970) Shape from shading; a method for obtaining the shape of a smooth opaque object from one view. PhD thesis, Massachusetts Institute of Technology

    Google Scholar 

  22. Hsu Y-F, Chang S-F (2010) Camera response functions for image forensics: An automatic algorithm for splicing detection. IEEE Trans Inf Forensics Secur 5(4):816–825

    Article  Google Scholar 

  23. Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the 7th workshop on multimedia and security, MM&Sec ’05, pp. 1–10, 2005

    Google Scholar 

  24. Johnson MK, Farid H (2006) Exposing digital forgeries through chromatic aberration. In: Proceedings of the 8th workshop on multimedia and security, MM&Sec ’06, pp 48–55. ACM, 2006

    Google Scholar 

  25. Johnson MK, Farid H (2006) Metric measurements on a plane from a single image. Technical Report TR2006-579, Department of Computer Science, Dartmouth College, 2006

    Google Scholar 

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

    Article  Google Scholar 

  27. Johnson MK, Farid H (2008) Exposing digital forgeries through specular highlights on the eye. In: Information Hiding of Lecture Notes in Computer Science, vol 4567. pp. 311–325, 2008

    Google Scholar 

  28. Pravin K, Sudha N, Ser W (2011) Exposing digital image forgeries by detecting discrepancies in motion blur. IEEE Trans Multimedia 13(3):443–452

    Google Scholar 

  29. Kee E, Farid H (2010) Exposing digital forgeries from 3-D lighting environments. In: IEEE International Workshop on Information Forensics and Security, Seattle, WA, 2010

    Google Scholar 

  30. Kirchner M (2008) Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. In: Proceedings of the 10th workshop on multimedia and security, MM&Sec ’08, pp 48–55. ACM, 2008

    Google Scholar 

  31. Zhouchen L, Rongrong W, Xiaoou T, Shum H-Y (2005) Detecting doctored images using camera response normality and consistency, In: IEEE Conference on Computer Vision and Pattern Recognition, 2005

    Google Scholar 

  32. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2(60):91–110

    Article  Google Scholar 

  33. Lukáš J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214

    Article  Google Scholar 

  34. Lyu S (2010) Estimating vignetting function from a single image for image authentication. In: MM&Sec ’10, pp 3–12, 2010

    Google Scholar 

  35. Mahajan D, Ramamoorthi R, Curless B (2008) A theory of frequency domain invariants: Spherical harmonic identities for BRDF/lighting transfer and image consistency. IEEE Trans Pattern Anal Mach Intell 30(2):197–213

    Article  Google Scholar 

  36. Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensics Secur 3(3):529–538

    Article  Google Scholar 

  37. Nillius P, Eklundh J-O (2001) Automatic estimation of the projected light source direction. In: Computer Vision and Pattern Recognition, 2001

    Google Scholar 

  38. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):758–767

    Article  Google Scholar 

  39. Pentland A (1982) Finding the illuminant direction. J Opt Soc Am 72(4):448–455

    Article  Google Scholar 

  40. Popescu A, Farid H (2004) Statistical tools for digital forensics. In: 6th International Workshop on Information Hiding, 2004

    Google Scholar 

  41. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Technical Report TR2004-515, Department of Computer Science, Dartmouth College, 2004

    Google Scholar 

  42. Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of re-sampling. IEEE Trans Signal Process 53(2):758–767

    Article  MathSciNet  Google Scholar 

  43. Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959

    Article  MathSciNet  Google Scholar 

  44. Ramamoorthi R, Hanrahan P (2001) An efficient representation for irradiance environment maps. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, SIGGRAPH ’01, pp 497–500. ACM Press, 2001

    Google Scholar 

  45. Ramamoorthi R, Hanrahan P (2001) On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. J Opt Soc Am A 18:2448–2559

    Article  MathSciNet  Google Scholar 

  46. Remondino F, El-Hakim S (2006) Image-based 3D modelling: A review. The Photogramm Rec 21(115):269–291

    Article  Google Scholar 

  47. Sinha P (2000) Perceiving illumination inconsistencies. In: Investigative Ophthalmology and Visual Science, vol 41(4):1192, 2000

    Google Scholar 

  48. Szeliski R (2011) Computer Vision: Algorithms and Applications. Springer, New York, 2011

    Google Scholar 

  49. Willson RG, Shafer SA (1994) What is the center of the image? J Opt Soc Am A 11(11):2946–2955

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  51. Zhang W, Cao X, Zhang J, Zhu J, Wang P (2009) Detecting photographic composites using shadows. In: IEEE Conference on Multimedia and Expo, pp 1042–1045, 2009

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Micah K. Johnson

Authors
  1. Micah K. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Micah K. Johnson .

Editor information

Editors and Affiliations

  1. , Computer Engineering Department, TOBB University of Economics and Technol, Sogutozu Cad. 43, Ankara, 06560, Turkey

    Husrev Taha Sencar

  2. Dept. Computer & Information Science, Polytechnic University, Brooklyn, 11201, New York, USA

    Nasir Memon

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Johnson, M. (2013). Detecting Doctored Images. In: Sencar, H., Memon, N. (eds) Digital Image Forensics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0757-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-0757-7_9

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-0756-0

  • Online ISBN: 978-1-4614-0757-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation