[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A robust approach to detect digital forgeries by exploring correlation patterns

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Local correlation pattern indicates integrity of an image. Exposing digital forgeries by detecting local correlation pattern of images has become an important kind of approach among many others. However, local correlation pattern is sensitive to JPEG compression, since compression can be regarded as a local homogenization and attenuates the characteristics of local correlation pattern. In this paper, rather than concentrating on the differences between image textures which is common in previous works, we specifically build a gaussian model to describe the local-correlation pattern of color filter array (CFA) interpolation. Thus the model will automatically adapt to JPEG compression. With the model built from the test image, the posterior probability map of CFA interpolation is calculated. To measure the trace of CFA, frequency characteristics of the posterior probability map are calculated and weighted combined. Then the image is classified as tampered or not by a simple threshold. Experimental results from over thousands of tampered images show the validity and efficiency of the proposed method. Moreover, we examine our algorithm to the problem of distinguishing computer-generated images from photos and detecting local tamper area. The proposed method shows a good performance in these tests.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Cao G, Zhao Y, Ni R, Tian H (2010) Anti-forensics of contrast enhancement in digital images. In: Proceedings of the 12th ACM workshop on multimedia and security, pp 25–34

  2. Stamm MC, Liu KJR (2011) Anti-forensics of digital image compression. IEEE Trans Inf Forensics Secur 6(3):1050–1065

    Article  Google Scholar 

  3. Katzenbeisser S, Petitolas F (1999) Information hiding techniques for steganography and digital watermaking, 1st edn. Artech House, p 240, 10/1999 - ISBN: 1580530354

  4. Cox I, Miller M, Bloom J, Honsinger C (2002) Digital watermarking. J Electron Imaging 11(3):414–414

    Article  Google Scholar 

  5. Lu W, Varna AL, Wu M (2010) Forensic hash for multimedia information. SPIE Media Forensics and Security, pp. 7541–0Y

  6. Battiato S, Farinella GM, Messina E, Puglisi G (2012) Robust image alignment for tampering detection. IEEE Trans Inf Forensics Secur 7(4):1105–1117

    Article  Google Scholar 

  7. Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 2:16–25

    Article  Google Scholar 

  8. Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25(6):389–399

    Article  Google Scholar 

  9. Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginner. Multimed Tools Appl 51(1):133–162

    Article  Google Scholar 

  10. Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop

  11. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Dartmouth College Tech. Rep, TR2004-515

  12. Mahdian B, Saic S (2007) Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2):180–189

    Article  Google Scholar 

  13. Lukáš J, Fridrich J, Goljan M (2006) Detecting digital image forgeries using sensor pattern noise. In: Proceedings of SPIE 6072, pp 362–372

  14. Hsu YF, Chang SF (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 

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

    Article  MathSciNet  Google Scholar 

  16. 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 

  17. He J, Lin Z, Wang L, Tang X (2006) Detecting doctored JPEG images via DCT coefficient analysis. ECCV 2006, pp 423–435

  18. Farid H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forensics Secur 4(1):154–160

    Article  MathSciNet  Google Scholar 

  19. Battiato S, Messina G (2009) Digital forgery estimation into DCT domain: a critical analysis. In: Proceedings of the first ACM workshop on multimedia in forensics 2009, pp 37–42

  20. Zach F, Riess C, Angelopoulou E (2012) Automated Image Forgery Detection through Classification of JPEG Ghosts. Pattern Recognition 2012, pp. 185–194

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

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

    Article  Google Scholar 

  23. Gallagher AC, Chen T (2008) Image authentication by detecting traces of demosaicing. CVPRW’08, pp 1–8

  24. Dirik AE, Memon N (2009) Image tamper detection based on demosaicing artifacts. ICIP’2009, pp 1497–1500

  25. Swaminathan A, Wu M, Liu KJR (2007) Nonintrusive component forensics of visual sensors using output images. IEEE Trans Inf Forensics Secur 2(1):91–106

    Article  Google Scholar 

  26. Cao H, Kot AC (2009) Accurate detection of demosaicing regularity for digital image forensics. IEEE Trans Inf Forensics Secur 4(4):899–910

    Google Scholar 

  27. Fan N, Jin C, Huang Y (2009) A pixel-based digital photo authentication framework via demosaicking inter-pixel correlation. In: Proceedings of the 11th ACM workshop on multimedia and security, pp 125–130

  28. Kirchner M (2010) Efficient estimation of CFA pattern configuration in digital camera images. In: Proceedings of SPIE, the International Society for Optical Engineering

  29. Takamatsu J, Matsushita Y, Ogasawara T, Ikeuchi K (2010) Estimating demosaicing algorithms using image noise variance. In: IEEE conference on computer vision and pattern recognition (CVPR), 2010, pp 279–286

  30. Schaefer G, Stich M (2004) UCID—an uncompressed colour image database. In: Proceedings SPIE, storage and retrieval methods and applications for multimedia 2004, San Jose, USA, pp 472–480

  31. Ng TT, Chang SF, Hsu J, Pepeljugoski M (2004) Columbia photographic images and photorealistic computer graphics dataset. Columbia University, ADVENT Technical Report, pp 205–2004

  32. Raph. http://www.raph.com. Accessed 28 March 2011

  33. 3dtotal. http://www.3dtotal.org. Accessed 28 March 2011

  34. Renderosity. http://www.renderosity.com. Accessed 28 March 2011

  35. Creative Crash. http://www.creativecrash.com. Accessed 28 March 2011

  36. 3dlinks. http://www.3dlinks.com/. Accessed 28 March 2011

Download references

Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program) under Grant No. 2012CB316400, Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (No. 20090201110029), the National Natural Science Foundation of China under Grant Nos. 61273252 and 61075007.

Author information

Authors and Affiliations

  1. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, No. 28, Xianning West Road, Xi’an, 710049, Shaanxi, People’s Republic of China

    Lu Li, Jianru Xue, Xiaofeng Wang & Lihua Tian

  2. School of Science, Xi’an University of Technology, No. 5, South Jinhua Road, Xi’an, 710048, Shaanxi, People’s Republic of China

    Xiaofeng Wang

Authors
  1. Lu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianru Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lihua Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianru Xue.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, L., Xue, J., Wang, X. et al. A robust approach to detect digital forgeries by exploring correlation patterns. Pattern Anal Applic 18, 351–365 (2015). https://doi.org/10.1007/s10044-013-0319-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-013-0319-9

Keywords

Search

Navigation