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Applied Intelligence

, Volume 48, Issue 7, pp 1791–1801 | Cite as

An efficient forensic technique for exposing region duplication forgery in digital images

  • Toqeer Mahmood
  • Zahid Mehmood
  • Mohsin Shah
  • Zakir Khan
Article

Abstract

The internet users share a massive amount of digital images daily. The accessibility of powerful image manipulation tools has made the integrity of image contents questionable. The most popular image tampering is to duplicate a region elsewhere in the same image to replicate or conceal some other region. The duplicated regions have identical color and texture attributes that make this artifact invisible to the human eye. Therefore, efficient techniques are required to verify the credibility of image contents by detecting the regions duplicated in the digital images. This paper proposes an efficient technique for exposing region duplication forgery in digital images. The proposed technique divides the approximation (LL) sub-band of shift invariant stationary wavelet transform into overlapping blocks of w × w (i.e. w = 4, 8) sizes. The distinctive features extracted from the overlapping blocks are utilized to expose the region duplication forgeries in digital images. The experimental results of the proposed technique are compared with state-of-the-art techniques that reveal the prominence, and effectiveness of the proposed technique in terms of precision, recall and F 1 score for different block sizes. Therefore, the proposed technique can reliably be applied to identify the counterfeited regions and the benefits of the proposed technique can be achieved in different fields for example crime investigation, news reporting, and judiciary.

Keywords

Digital forensics Region duplication Copy-move Image tampering Passive authentication 

Notes

Compliance with Ethical Standards

Conflict of interests

All authors declare that there are no conflict of interests regarding the publication of this paper.

References

  1. 1.
    Qureshi MA, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39:46–74CrossRefGoogle Scholar
  2. 2.
    Asghar K, Habib Z, Hussain M (2017) Copy-move and splicing image forgery detection and localization techniques: a review. Aust J Forensic Sci 49(3):281–307Google Scholar
  3. 3.
    Qi X, Xin X (2015) A singular-value-based semi-fragile watermarking scheme for image content authentication with tamper localization. J Vis Commun Image Represent 30:312–327CrossRefGoogle Scholar
  4. 4.
    Bausvs R, Kriukovas A (2008) Digital signature approach for image authentication. Electron Electr Eng 6(86):65–68Google Scholar
  5. 5.
    Qazi T, Hayat K, Khan SU, Madani SA, Khan I, Kołodziej J, Li H, Lin W, Yow K, Xu C-Z (2013) Survey on blind image forgery detection. IET Image Process 7(7):660–670CrossRefGoogle Scholar
  6. 6.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867CrossRefGoogle Scholar
  7. 7.
    Amerini I, Barni M, Caldelli R, Costanzo A (2013) Counter-forensics of SIFT-based copy-move detection by means of keypoint classification. EURASIP J Image Video Process 2013(1):18CrossRefGoogle Scholar
  8. 8.
    Alkawaz MH, Sulong G, Saba T, Rehman A (2016) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2663-3
  9. 9.
    Zimba M, Xingming S (2011) DWT-PCA(EVD) based copy-move image forgery detection. Int J Digital Content Technol Appl 5(1):251–258Google Scholar
  10. 10.
    Hayat K, Qazi T (2017) Forgery detection in digital images via discrete wavelet and discrete cosine transforms. Comput Electr Eng.  https://doi.org/10.1016/j.compeleceng.2017.03.013
  11. 11.
    Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop. CiteseerGoogle Scholar
  12. 12.
    Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. TR2004-515, Technical Report, Dartmouth CollegeGoogle Scholar
  13. 13.
    Myna A, Venkateshmurthy M, Patil C (2007) Detection of region duplication forgery in digital images using wavelets and log-polar mapping. In: International conference on computational intelligence and multimedia applications, 2007. IEEE, pp 371–377Google Scholar
  14. 14.
    Christlein V, Riess C, Angelopoulou E (2010) A Study on Features for the Detection of Copy-Move Forgeries. In: Sicherheit, pp 105–116Google Scholar
  15. 15.
    Ryu S-J, Lee M-J, Lee H-K (2010) Detection of copy-rotate-move forgery using zernike moments. In: 12th international conference on information hiding. Springer, Berlin, pp 51–65Google Scholar
  16. 16.
    Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206(1):178–184CrossRefGoogle Scholar
  17. 17.
    Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9(1):49–57CrossRefGoogle Scholar
  18. 18.
    Sekeh MA, Maarof MA, Rohani MF, Mahdian B (2013) Efficient image duplicated region detection model using sequential block clustering. Digit Investig 10(1):73–84CrossRefGoogle Scholar
  19. 19.
    Lynch G, Shih FY, Liao H-YM (2013) An efficient expanding block algorithm for image copy-move forgery detection. Inf Sci 239:253–265CrossRefGoogle Scholar
  20. 20.
    Wu Y, Deng Y, Duan H, Zhou L (2014) Dual tree complex wavelet transform approach to copy-rotate-move forgery detection. Sci China Inf Sci 57(1):1–12Google Scholar
  21. 21.
    Alahmadi A, Hussain M, Aboalsamh H, Muhammad G, Bebis G, Mathkour H (2017) Passive detection of image forgery using DCT and local binary pattern. Signal Image Video Process 11(1): 81–88Google Scholar
  22. 22.
    Ulutas G, Ustubioglu B, Ulutas M, Nabiyev V (2017) Frame duplication/mirroring detection method with binary features. IET Image Process 11(5):333–342CrossRefGoogle Scholar
  23. 23.
    Lee J-C (2015) Copy-move image forgery detection based on Gabor magnitude. J Vis Commun Image Represent 31:320–334CrossRefGoogle Scholar
  24. 24.
    Nason GP, Silverman BW (1995) The stationary wavelet transform and some statistical applications. Lecture notes in statistics. Springer, New York, pp 281–281Google Scholar
  25. 25.
    Pesquet J-C, Krim H, Carfantan H (1996) Time-invariant orthonormal wavelet representations. IEEE Trans Signal Process 44(8):1964–1970CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringUniversity of Engineering and TechnologyTaxilaPakistan
  2. 2.Department of Software EngineeringUniversity of Engineering and TechnologyTaxilaPakistan
  3. 3.Department of Information TechnologyHazara UniversityMansehraPakistan
  4. 4.School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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