Skip to main content

Advertisement

Springer Nature Link
Log in

A probabilistic framework for copy-move forgery detection based on Markov Random Field

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Copy-move forgery is one of the most common kind of image tampering where some part of an image is copied, may be with minor modifications, pasted to another area of the same image. With the growing usage of images in todays life, image authenticity has become a vital issue and consequently many image forgery detection techniques have been presented. In this paper, for the first time, we propose to treat copy-move forgery detection as labeling problem in a Markov Random Field. To gain a proper balance between precision and speed, an over segmentation is performed as a preprocessing step to obtain superpixels which are then regarded as nodes of the markov network. Intelligent selection of unary and binary potentials let the maximum a posteriori labeling to be a precise map of the forged regions. Qualitative and quantitative comparison with the state-of-the-art methods using public benchmarks demonstrate that the proposed method can improve precision while keeping the processing demands low.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P (2010) SLIC Superpixels EPFL Technical Report 149300

  2. Alkawaz MH, Sulong G, Saba T, Rehman A (2018) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput Applic 30(1):183–192

    Article  Google Scholar 

  3. Amerini I, Ballan L, Caldelli R, Del-Bimo A, Serra G (2011) A SIFT-Based forensic method for copy-move attack detection and transformation recovery. IEEE Trans Inf Secur 6(3):1099–1110

    Article  Google Scholar 

  4. Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094

    Article  Google Scholar 

  5. Bashar M, Knoda N, Ohnishi K, Mori (2010) Exploring duplicated regions in natural images, IEEE Transactions on Image Processing in press

  6. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) peeded-up robust features (SURF). Comput Vis Image Understand 110(3):346–359

    Article  Google Scholar 

  7. Bayram S, Taha Sencar H, Memon N (2009) In IEEE ICASSP, Washington, DC, USA

  8. Besag J (1986) On the statistical analysis of dirty pictures. J R Statist Soc 48 (3):259–302

    MathSciNet  MATH  Google Scholar 

  9. BhavyaBhanu MP, ArunKumar MN (2017) Copy-move forgery detection using segmentation. In: 11th International Conference on Intelligent Systems and Control (ISCO), 5–6 Jan. 2017, Coimbatore, pp 228–224

  10. Bi X, Pun C-M, Yuan X-C (2018) Multi-scale feature extraction and adaptive matching for copy-move forgery detection. Multimed Tools Appl 77:363–385

    Article  Google Scholar 

  11. Chen B, Yu M, Su Q, Li L (2018) Fractional quaternion cosine transform and its application in color image copy-move forgery detection. Multimedia Tools and Applications

  12. Dixit R, Naskar R, Mishra S (2017) Blur-invariant copy-move forgery detection technique with improved detection accuracy utilising SWT-SVD. IET Image Process 11(5):301–309

    Article  Google Scholar 

  13. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741

    Article  MATH  Google Scholar 

  14. Haghighi B, Taherinia AH, Harati A (2018) TRLH: Fragile and blind dual watermarking for image tamper detection and self-recovery based on lifting wavelet transform and halftoning technique. J Vis Commun Image Represent 50:49–64

    Article  Google Scholar 

  15. Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206:178–184

    Article  Google Scholar 

  16. Koller D, Friedman N (2009) Probabilistic graphical models, Massachusetts Institute of Technology

  17. Lee J, Chang CH, Chen W (2015) Detection of copy–move image forgery using histogram of Information Sciences

  18. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Article  Google Scholar 

  19. Li L, Li S, Zhu H, Chu S-C, Roddick J F, Pan J-S (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inf Hiding Multimedia Signal Process 4(1):46–56

    Google Scholar 

  20. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224:59–67

    Article  Google Scholar 

  21. Li Y, Zhou J (2018) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY

  22. Lowe D (2004) Distinctive image features. International Journal of Computer Vision

  23. Minakshi K (2003) Digital image processing. In: Satellite remote sensing and GIS applications in agricultural meteorology, world meteorological organization publishing, pp 81–102

  24. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867

    Article  Google Scholar 

  25. Park C, Choeh JY (2017) Fast and robust copy-move forgery detection based on scale-space representation. Multimed Tools Appl 77(13):16795–16811

    Article  Google Scholar 

  26. Rangarajan A, Chellappa R (1995) Markov random fileds in image processing, Department of Computer Science, Yale University

  27. Ryu S-J, Kirchner M, Lee M-J, Lee H-K (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8(8):1355–1370

    Article  Google Scholar 

  28. Sekhar R, Shaji R (2016) A study on segmentation-based copy-move forgery detection using DAISY descriptor. In: Proceedings of the international conference on soft computing systems, pp 223–233

  29. Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: Exploring image. J Vis Commun Image R 29(C):16–32

    Article  Google Scholar 

  30. Soni B, Das PK, Thounaojam DM (2019) Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features. Journal of Information Security and Applications

  31. Swaminathan A, Wu M, Liu KR (2008) Digital image forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 3:101–117

    Article  Google Scholar 

  32. Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD - new database for copy-move forgery detection. In: ELMAR, 55th international symposium, pp 49–54

  33. Vedaldi A, Fulkerson B VLFeat : An open and portabale library of computer vision algorithms, 2008. [Online]. Available: http://www.vlfeat.org

  34. Satheesh S, Thomas A, Devasia A (2017) Image forensic, copy-move forgery, SURF. Int J Eng Trends Technol (IJETT) V45(6):285–287

    Google Scholar 

  35. Yang B, Sun X, Guo H, Xia ZH, Chen X (2018) A copy-move forgery detection method based on CMFD-SIFT. Multimed Tools Appl 77(1):837–855

    Article  Google Scholar 

  36. Yanga F, Lia J, Lua W, Wengb J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intel 59:73–83

    Article  Google Scholar 

  37. Yeap YY, Sheikh UU, Rahman A (2018) Image forensic for digital image copy move forgery detection. In: Signal processing and its applications (CSPA), Malaysia, Malaysia

  38. Zandi M, Aznave A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans Inf Forensics Secur 11(11):2499–2512

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Behnaz Elhaminia, Ahad Harati & Amirhossein Taherinia

Authors
  1. Behnaz Elhaminia
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ahad Harati
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Amirhossein Taherinia
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ahad Harati.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elhaminia, B., Harati, A. & Taherinia, A. A probabilistic framework for copy-move forgery detection based on Markov Random Field. Multimed Tools Appl 78, 25591–25609 (2019). https://doi.org/10.1007/s11042-019-7713-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7713-2

Keywords