Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique

Abstract

The wide availability of easy-to-use image editors has made the authenticity of images questionable. Copy-move is one of the most applied forgery types. A new copy-move forgery detection and localization technique independent from the characteristics of the forged regions is proposed in this paper. SIFT keypoints are obtained from CLAHE applied sub-images extracted from the input image by using RGB and L*a*b* color-spaces. Keypoint matching is realized on the sub-images and duplicated regions are determined roughly to create roughly marked image R. RANSAC is also applied in this stage and generated homography matrix is used to construct transformed roughly marked image R. The method extracts DCT based features from R and R to localize exact borders of the tampered regions on the roughly determined areas by using a dynamic threshold. The proposed method has a new suggestion to determine the threshold dynamically. Tamper localization procedure also utilizes from morphological operations (chosen depending on the characteristic of the image) and Connected Component Labeling to determine exact forge boundaries. Results indicate that the proposed method has a better performance compared with state-of-the-art copy-move forgery detection methods on the GRIP dataset. Scaling attack performance of the method is especially better than similar works as shown in the results.

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References

  1. 1.

    Alberry HA, Hegazy AA, Salama GI (2018) A fast SIFT based method for copy move forgery detection. Futur Comput Informatics J 3(2):159–165

    Article  Google Scholar 

  2. 2.

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

    Article  Google Scholar 

  3. 3.

    Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28(6):659–1669

    Article  Google Scholar 

  4. 4.

    Bayram S, Sencar HT, Memon N, Efficient A, Robust Method For Detecting Copy-Move Forgery (2009) IEEE International Conference on Acoustics, Speech and Signal Processing. Proceeding Book, New York, pp 1053–1056

    Google Scholar 

  5. 5.

    Bi X, Pun C, Yuan X (2016) Multi-level dense descriptor and hierarchical feature matching for copy–move Forgery detection. Inf Sci 345:226–242

    Article  Google Scholar 

  6. 6.

    Bo X, JunwenW, Guangjie L, Yuewei D (2010) Image copy-move forgery detection based on SURF. Proc Int Conf Multimedia Inf Netw Secur (MINES), pp 889–892

  7. 7.

    Bravo-Solorio S, Nandi AK (2011) Exposing Duplicated Regions Affected by Reflection, Rotation and Scaling, Intl. Conference on Acoustics, Speech and Signal Processing. Proceeding Book, Prague, pp 1880–1883

    Google Scholar 

  8. 8.

    Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7:1841–1854

    Article  Google Scholar 

  9. 9.

    Cozzolino D, Poggi G, Verdoliva L (2014) “Copy-move forgery detection based on PatchMatch,” in Proc. IEEE Int Conf Image Process, pp 5312–5316

  10. 10.

    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297

    Article  Google Scholar 

  11. 11.

    Emam M, Han Q, Niu X (2016) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 75(18):11513–11527

    Article  Google Scholar 

  12. 12.

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

    MathSciNet  Article  Google Scholar 

  13. 13.

    Fridrich J, Soukal D, Lukas J (2003) “Detection of copy move forgery in digital images,” in Digital Forensic Research Workshop (DFRWS ’03), pp 1–10

  14. 14.

    Haralick RM, Shapiro L (1992) “Connected component labeling”, Computer and Robot Vision, Vol. 1. Addison-Wesley, New York

    Google Scholar 

  15. 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. 16.

    Kasson JK, Plouffe W (1992) An analysis of selected computer interchange color spaces. ACM Trans Graph 11(4):373–405

    Article  Google Scholar 

  17. 17.

    Kundur D, Hatzinakos D (1999) Digital watermarking for telltale tamper proofing and authentication. Proc IEEE 87(7):1167–1180

    Article  Google Scholar 

  18. 18.

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

    Article  Google Scholar 

  19. 19.

    Li L, Li S, Zhu H (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inf Hid Multimed Signal Process 4(1):46–56

    Google Scholar 

  20. 20.

    Li Y, Zhou J (2016) “Image copy-move forgery detection using hierarchical feature point matching,” in Proc. Asia–Pacific Signal Inf Process Assoc Annu Summit Conf (APSIPA ASC), pp 1–4

  21. 21.

    Lian S, Kanellopoulos D (2009) Recent advances in multimedia information system security. Inform 33:3–24

    MathSciNet  Google Scholar 

  22. 22.

    Luo W, Huang J, Qiu G (2009) Robust Detection of Region-Duplication Forgery in Digital Images, International Conference on Pattern Recognition, vol 4. Proceeding Book, Hong Kong, pp 746–749

    Google Scholar 

  23. 23.

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

    Google Scholar 

  24. 24.

    Om A, Be K (2013) Passive detection of copy-move Forgery in digital images: state-of-the-art. Forensic Sci Int 231:284–295

    Article  Google Scholar 

  25. 25.

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

  26. 26.

    Pun CM, Chung JL (2018) A two-stage localization for copy-move forgery detection. Inf Sci (Ny) 463–464:33–55

    MathSciNet  Article  Google Scholar 

  27. 27.

    Pun C, Yuan X, Bi X (Aug. 2015) Image Forgery detection using adaptive Oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716

    Article  Google Scholar 

  28. 28.

    Rey C, Dugelay JL (2002) A survey of watermarking algorithms for image authentication. EURASIP J Appl Signal Process:613–621

  29. 29.

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

    Article  Google Scholar 

  30. 30.

    Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent 29:16–32

    Article  Google Scholar 

  31. 31.

    Wang J, Liu G, Li H, Dai Y, Wang Z (2009) “Detection of image region duplication forgery using model with circle block,” In Proceedings of the 1st International Conference on Multimedia Information Networking and Security (MINES ‘09), pp. 25–29. IEEE, Hubei, China

    Google Scholar 

  32. 32.

    Wenchang S, Fei Z, Bo Q, Bin L (2016) Improving image copy-move forgery detection with particle swarm optimization techniques. Proc China Commun 13(1):139–149

    Article  Google Scholar 

  33. 33.

    Wu Q, Wang S, Zhang X (2011) Log-Polar Based Scheme for Revealing Duplicated Regions in Digital Images. In IEEE Signal Process Lett 18(10):559–562

    Article  Google Scholar 

  34. 34.

    Yang F, Li J, Lu W, Weng J Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83

  35. 35.

    Zandi M, Mahmoudi-Aznaveh A, Mansouri A (2014) Adaptive matching for copy move forgery detection, 2014. IEEE International Workshop on Information Forensics and Security (WIFS), pp 119–124

  36. 36.

    Zhang J, Feng Z, Su Y (2008) “A new approach for detecting copy move forgery in digital images,” in Proc Int Conf Communication Systems, pp 362–366

  37. 37.

    Zhu Y, Shen X, Chen H (2015) Copy-move Forgery detection based on scaled ORB. Multimed Tools Appl 75(6):1–15

    Google Scholar 

  38. 38.

    Zuiderveld K (1994) “Contrast Limited Adaptive Histogram Equalization.” Chapter VIII.5, Graphics Gems IV. In: Heckbert PS (ed) Chapter VIII.5, Graphics Gems IV. Cambridge, Academic Press, pp 474–485

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Funding

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with Project No: 119E045.

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Correspondence to Gul Tahaoglu.

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Tahaoglu, G., Ulutas, G., Ustubioglu, B. et al. Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-020-10241-9

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Keywords

  • L*a*b* color space
  • Copy move forgery
  • Dynamic localization