Computer Vision

Living Edition

Image Forensics

  • Hany FaridEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-030-03243-2_877-1
  • 15 Downloads

Synonyms

Related Concepts

Definition

Image forensics refers to the analysis of an image to determine if it has been manipulated from the time of its recording. The techniques described here – so called passive techniques – operate in the absence of digital watermarks, signatures, or specialized hardware. Instead, these techniques analyze physical, geometric, optical, sensor, and file properties for inconsistencies that may arise from image manipulation.

Background

History has shown that many autocratic leaders had photographs manipulated in an attempt to rewrite history. These men understood the power of photography and that if they changed photographs they could change history. Cumbersome and time-consuming darkroom techniques were required to alter the historical record on behalf of Stalin and others. Today, powerful and low-cost digital technology coupled with sophisticated rendering and synthesis...

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

References

  1. 1.
    Farid H (2016) Photo forensics. MIT Press, CambridgeCrossRefGoogle Scholar
  2. 2.
    O’Brien J, Farid H (2012) Exposing photo manipulation with inconsistent reflections. ACM Trans Graph 31(1):4:1–4:11CrossRefGoogle Scholar
  3. 3.
    Kee E, O’Brien J, Farid H (2014) Exposing photo manipulation from shading and shadows. ACM Trans Graph 33(5):165:1–165:21CrossRefGoogle Scholar
  4. 4.
    Popescu A, Farid H (2010) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959MathSciNetCrossRefGoogle Scholar
  5. 5.
    Fridrich J, Lukas J, Goljan M (2006) Digital camera identification from sensor noise. IEEE Trans Inf Secur Forensic 1(2):205–214CrossRefGoogle Scholar
  6. 6.
    Kirchner M (2010) Efficient estimation of CFA pattern configuration in digital camera images. In: Media forensics and security II. International Society for Optics and Photonics, vol 7541, p 754111Google Scholar
  7. 7.
    Kee E, Johnson M, Farid H (2011) Digital image authentication from JPEG headers. IEEE Trans Inf Forensics Secur 7(3):1066–1075CrossRefGoogle Scholar
  8. 8.
    Gloe T (2012) Forensic analysis of ordered data structures on the example of JPEG files. In: IEEE workshop on information forensics and securityCrossRefGoogle Scholar
  9. 9.
    Popescu A, Farid H (2004) Statistical tools for digital forensics. In: International workshop on information hidingCrossRefGoogle Scholar
  10. 10.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867CrossRefGoogle Scholar
  11. 11.
    Bianchi T, Piva A (2011) Analysis of non-aligned double JPEG artifacts for the localization of image forgeries. In: IEEE workshop on information forensics and securityCrossRefGoogle Scholar
  12. 12.
    Liu M-Y, Breuel T, Kautz J (2018) Unsupervised image-to-image translation networks. In: Neural information processing systems, pp 700–708Google Scholar
  13. 13.
    Suwajanakorn S, Seitz SM, Kemelmacher-Shlizerman I (2017) Synthesizing Obama: learning lip sync from audio. ACM Trans Graph 36(4):95CrossRefGoogle Scholar
  14. 14.
    Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE international conference on computer visionCrossRefGoogle Scholar
  15. 15.
    Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Wade-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CaliforniaBerkeleyUSA

Section editors and affiliations

  • Rama Chellappa
    • 1
  1. 1.University of MarylandCollege ParkUSA