Skip to main content
SpringerLink
Log in

A copy-move image forgery detection technique based on Gaussian-Hermite moments

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

Abstract

Images are one of the most prominently used digital information sharing medium now a days. Due to availability of state-of-the-art image editing tools it has become very easy to forge an image. Among various types of image forgeries, copy-move (region-duplication) forgery cases are emerging very frequently. In copy-move image forgery one or more regions of an image are replicated within the same image. In this paper, a new robust copy-move image forgery detection technique is proposed using Gaussian-Hermite Moments (GHM). The proposed technique divides the input image into overlapping blocks of fixed size and then the Gaussian-Hermite moments are extracted for each block. The matching of similar blocks is done by sorting all the features lexicographically. The experimental results show that the proposed technique can locate the copy-move forged regions in a forged image very accurately. The proposed technique shows promising results in the presence of various post-processing operations scaling, blurring, color reduction, adjustment of brightness, rotation, and JPEG compression.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. 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):81–88

    Google Scholar 

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

    Google Scholar 

  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):659–669

    Google Scholar 

  4. Ansari MD, Ghrera SP, Tyagi V (2014) Pixel-based image forgery detection: a review. IETE J Educ 55(1):40–46

    Google Scholar 

  5. 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

    Google Scholar 

  6. Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Trans Image Process 99:1–40

    Google Scholar 

  7. Belghini N, Kharroubi J (2012) 3D face recognition using Gaussian Hermite moments. Int J Comput Appl 0975(888):3–6

    Google Scholar 

  8. Bi X, Pun CM (2018) Fast copy-move forgery detection using local bidirectional coherency error refinement. Pattern Recogn 81:161–175

    Google Scholar 

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

    Google Scholar 

  10. Bi X, Pun C, Yuan X (2016) Multi-level dense descriptor and hierarchical feature matching for copy – move forgery detection. Inf. Sci. (Ny). 345:1–17

    Google Scholar 

  11. Bravo SS and Nandi AK (2011) Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics, in Proc. Int.Conf. Acoustics, Speech and Signal Processing, pp. 1880–1883

  12. Chen L, Lu W, Ni J, Sun W, Huang J (2013) Region duplication detection based on Harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254

    Google Scholar 

  13. 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(6):1841–1854

    Google Scholar 

  14. Cozzolino D, Poggi G, and Verdoliva L (2014) Copy-move forgery detection based on PatchMatch, in IEEE International Conference on Image Processing, pp. 5312–5316

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

    Google Scholar 

  16. 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  Google Scholar 

  17. Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Digit Forensic Res Work 3:652–663

    Google Scholar 

  18. Gürbüz E, Ulutaş G, and Ulutaş M (2015) Rotation Invariant Copy Move Forgery Detection Method, in Proceedings of the 9th International Conference on Electrical and Electronics Engineering (ELECO), pp. 202–206

  19. Hosny KM, Hamza HM, Lashin NA (2018) Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators. Imaging Sci J 66(6):1–16

    Google Scholar 

  20. Isaac MM, Wilscy M (2018) Image forgery detection using region - based rotation invariant co-occurrences among adjacent LBPs. J Intell Fuzzy Syst 34(3):1679–1690

    Google Scholar 

  21. Lee JC, Chang CP, Chen WK (2015) Detection of copy-move image forgery using histogram of orientated gradients. Inf. Sci. (Ny). 321:250–262

    Google Scholar 

  22. 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

    Google Scholar 

  23. Liu XWY, Xu H, and Wang P (2016) Robust copy – move forgery detection using quaternion exponent moments, Pattern Anal. Appl.

  24. Liu Y, Guan Q, Zhao X (2017) Copy-move forgery detection based on convolutional kernel network. Multimed Tools Appl 77:1–25

    Google Scholar 

  25. Luo W, Jiwu H (2006) Robust detection of region-duplication forgery in digital image. 18th Int Conf Pattern Recognit 4:746–749

    Google Scholar 

  26. Ma X, Pan R, and Wang L (2010) License plate character recognition based on Gaussian-Hermite moments, 2nd Int. Work. Educ. Technol. Comput. Sci. ETCS 2010, vol. 3, no. c, pp. 11–14

  27. Mahmood T, Mehmood Z, Shah M, Saba T (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J Vis Commun Image Represent 53:202–214

    Google Scholar 

  28. Manu VT, Mehtre BM (2018) Copy-move tampering detection using affine transformation property preservation on clustered keypoints. Signal, Image Video Process. 12(3):549–556

    Google Scholar 

  29. Meena KB and Tyagi V (2019) Image Forgery Detection : Survey and Future directions, in Data, Engineering and applications, vol.2, Springer Singapore, pp. 163–194, https://doi.org/10.1007/978-981-13-6351-1_14

    Google Scholar 

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

    Google Scholar 

  31. Popescu A and Farid H (2004) Exposing Digital Forgeries by Detecting Duplicated Image Regions, Dartmouth College, Computer Science, Tech. Rep. TR2004–515

  32. Prakash CS, Kumar A, Maheshkar S, and Maheshkar V (2018) An integrated method of copy-move and splicing for image forgery detection, Multimed. Tools Appl., pp. 1–25

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

    MathSciNet  Google Scholar 

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

    Google Scholar 

  35. Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans. Inf. Forensics Secur. 8(8):1355–1370

    Google Scholar 

  36. Shen J (1997) Orthogonal Gaussian–Hermite moments for image characterization. In: SPIE intelligent robots computer vision XVI, Pitts- burgh, pp 224–233

    Google Scholar 

  37. Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205

    Google Scholar 

  38. 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

    Google Scholar 

  39. Tralic D, Zupancic I, Grgic S, and Grgic M (2013) CoMoFoD - New Database for Copy-Move Forgery Detection, in Proceedings of 55th International Symposium ELMAR-2013, pp. 25–27

  40. Tralic D, Rosin PL, Sun X and Grgic S (2014) Detection of Duplicated Image Regions using Cellular Automata, in Proceedings of the International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 167–170

  41. Tralic D, Grgic S, Sun X, Rosin PL (2016) Combining cellular automata and local binary patterns for copy-move forgery detection. Multimed Tools Appl 75(24):16881–16903

    MATH  Google Scholar 

  42. Tyagi V (2018) Understanding Digital Image Processing. CRC Press

  43. Ustubioglu B, Ulutas G, Ulutas M, Nabiyev VV (2016) A new copy move forgery detection technique with automatic threshold determination. AEU - Int J Electron Commun 70(8):1076–1087

    Google Scholar 

  44. Wang X, Li S, Liu Y (2016) A new keypoint-based copy-move forgery detection for small smooth regions. Multimed Tools Appl 76(22):23353–23382

    Google Scholar 

  45. Wu Y, Shen J (2004) Moving object detection using orthogonal Gaussian-Hermite moments. Vis Commun Image Process 5308:841–849

    Google Scholar 

  46. Xu B, Wang J, Liu G, and Dai Y (2010) Image copy-move forgery detection based on SURF, in Proceedings - 2nd International Conference on Multimedia Information Networking and Security, MINES 2010, pp. 889–892

  47. Yang B, Li G, Zhang H, Dai M (2011) Rotation and translation invariants of Gaussian-Hermite moments. Pattern Recogn Lett 32(9):1283–1298

    Google Scholar 

  48. Yang B, Sun X, Chen X, Zhang J, Li X (2013) An efficient forensic method for copy-move forgery detection based on DWT-FWHT. Radioengineering 22(4):1098–1105

    Google Scholar 

  49. Yang B, Kostková J, Flusser J, Suk T (2017) Scale invariants from Gaussian–Hermite moments. Signal Process 132:77–84

    Google Scholar 

  50. Ying Yang H, Niu Y, Xian Jiao L, Nan Liu Y, Yang Wang X, and Li Zhou Z (2017) Robust copy-move forgery detection based on multi-granularity Superpixels matching, Multimed. Tools Appl., pp. 1–27

  51. Youfu W, Jun S (2005) Properties of orthogonal Gaussian-Hermite moments and their applications. EURASIP J Appl Signal Processing (4):588–599

  52. Zandi M, Mahmoudi-Aznaveh 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

    Google Scholar 

  53. Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233:158–166

    Google Scholar 

  54. Zhao J, Zhao W (2013) Passive forensics for region duplication image forgery based on Harris feature points and local binary patterns. Math Probl Eng 4:1–12

    Google Scholar 

  55. Zhong J, Gan Y (2016) Detection of copy – move forgery using discrete analytical Fourier – Mellin transform. Nonlinear Dyn 84(1):189–202

    MathSciNet  MATH  Google Scholar 

  56. Zhu Y, Shen X, Chen H (2016) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75(6):3221–3233

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jaypee University of Engineering and Technology, Raghogarh, Guna, MP, India

    Kunj Bihari Meena & Vipin Tyagi

Authors
  1. Kunj Bihari Meena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vipin Tyagi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vipin Tyagi.

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

Meena, K.B., Tyagi, V. A copy-move image forgery detection technique based on Gaussian-Hermite moments. Multimed Tools Appl 78, 33505–33526 (2019). https://doi.org/10.1007/s11042-019-08082-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08082-2

Keywords

Search

Navigation