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

  • Kunj Bihari Meena
  • Vipin TyagiEmail author
Article
  • 53 Downloads

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.

Keywords

Gaussian-Hermite moments Image forgery Image forgery detection Copy-move forgery Key-point Passive forgery detection Post-processing Tampering detection 

Notes

References

  1. 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):81–88CrossRefGoogle Scholar
  2. 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):1099–1110CrossRefGoogle 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):659–669CrossRefGoogle Scholar
  4. 4.
    Ansari MD, Ghrera SP, Tyagi V (2014) Pixel-based image forgery detection: a review. IETE J Educ 55(1):40–46CrossRefGoogle Scholar
  5. 5.
    Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of Keypoints. IEEE Trans. Inf. Forensics Secur. 10(10):2084–2094CrossRefGoogle Scholar
  6. 6.
    Bashar M, Noda K, Ohnishi N, Mori K (2010) Exploring duplicated regions in natural images. IEEE Trans Image Process 99:1–40Google Scholar
  7. 7.
    Belghini N, Kharroubi J (2012) 3D face recognition using Gaussian Hermite moments. Int J Comput Appl 0975(888):3–6Google Scholar
  8. 8.
    Bi X, Pun CM (2018) Fast copy-move forgery detection using local bidirectional coherency error refinement. Pattern Recogn 81:161–175CrossRefGoogle Scholar
  9. 9.
    Bi X, Pun CM, Yuan XC (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf. Sci. (Ny). 345:226–242CrossRefGoogle Scholar
  10. 10.
    Bi X, Pun C, Yuan X (2016) Multi-level dense descriptor and hierarchical feature matching for copy – move forgery detection. Inf. Sci. (Ny). 345:1–17CrossRefGoogle Scholar
  11. 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. 1880–1883Google Scholar
  12. 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):244–254CrossRefGoogle Scholar
  13. 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):1841–1854CrossRefGoogle Scholar
  14. 14.
    Cozzolino D, Poggi G, and Verdoliva L (2014) Copy-move forgery detection based on PatchMatch, in IEEE International Conference on Image Processing, pp. 5312–5316Google Scholar
  15. 15.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans. Inf. Forensics Secur. 10(11):2284–2297CrossRefGoogle Scholar
  16. 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):381–395MathSciNetCrossRefGoogle Scholar
  17. 17.
    Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Digit Forensic Res Work 3:652–663Google Scholar
  18. 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. 202–206Google Scholar
  19. 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):1–16CrossRefGoogle Scholar
  20. 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):1679–1690CrossRefGoogle Scholar
  21. 21.
    Lee JC, Chang CP, Chen WK (2015) Detection of copy-move image forgery using histogram of orientated gradients. Inf. Sci. (Ny). 321:250–262CrossRefGoogle Scholar
  22. 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):507–518CrossRefGoogle Scholar
  23. 23.
    Liu XWY, Xu H, and Wang P (2016) Robust copy – move forgery detection using quaternion exponent moments, Pattern Anal. Appl. Google Scholar
  24. 24.
    Liu Y, Guan Q, Zhao X (2017) Copy-move forgery detection based on convolutional kernel network. Multimed Tools Appl 77:1–25Google Scholar
  25. 25.
    Luo W, Jiwu H (2006) Robust detection of region-duplication forgery in digital image. 18th Int Conf Pattern Recognit 4:746–749Google Scholar
  26. 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. 11–14Google Scholar
  27. 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 53:202–214CrossRefGoogle Scholar
  28. 28.
    Manu VT, Mehtre BM (2018) Copy-move tampering detection using affine transformation property preservation on clustered keypoints. Signal, Image Video Process. 12(3):549–556CrossRefGoogle Scholar
  29. 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
  30. 30.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans. Inf. Forensics Secur. 5:857–867CrossRefGoogle Scholar
  31. 31.
    Popescu A and Farid H (2004) Exposing Digital Forgeries by Detecting Duplicated Image Regions, Dartmouth College, Computer Science, Tech. Rep. TR2004–515Google Scholar
  32. 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. 1–25Google Scholar
  33. 33.
    Pun CM, Chung JL (2018) A two-stage localization for copy-move forgery detection. Inf Sci (Ny) 463–464:33–55MathSciNetCrossRefGoogle Scholar
  34. 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):1705–1716CrossRefGoogle Scholar
  35. 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):1355–1370CrossRefGoogle Scholar
  36. 36.
    Shen J (1997) Orthogonal Gaussian–Hermite moments for image characterization. In: SPIE intelligent robots computer vision XVI, Pitts- burgh, pp 224–233Google Scholar
  37. 37.
    Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205Google Scholar
  38. 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 29:16–32CrossRefGoogle Scholar
  39. 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. 25–27Google Scholar
  40. 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. 167–170Google Scholar
  41. 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):16881–16903zbMATHCrossRefGoogle Scholar
  42. 42.
    Tyagi V (2018) Understanding Digital Image Processing. CRC PressGoogle Scholar
  43. 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):1076–1087CrossRefGoogle Scholar
  44. 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):23353–23382CrossRefGoogle Scholar
  45. 45.
    Wu Y, Shen J (2004) Moving object detection using orthogonal Gaussian-Hermite moments. Vis Commun Image Process 5308:841–849Google Scholar
  46. 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. 889–892Google Scholar
  47. 47.
    Yang B, Li G, Zhang H, Dai M (2011) Rotation and translation invariants of Gaussian-Hermite moments. Pattern Recogn Lett 32(9):1283–1298CrossRefGoogle Scholar
  48. 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):1098–1105Google Scholar
  49. 49.
    Yang B, Kostková J, Flusser J, Suk T (2017) Scale invariants from Gaussian–Hermite moments. Signal Process 132:77–84CrossRefGoogle Scholar
  50. 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. 1–27Google Scholar
  51. 51.
    Youfu W, Jun S (2005) Properties of orthogonal Gaussian-Hermite moments and their applications. EURASIP J Appl Signal Processing (4):588–599Google Scholar
  52. 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):2499–2512CrossRefGoogle Scholar
  53. 53.
    Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233:158–166CrossRefGoogle Scholar
  54. 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 4:1–12Google Scholar
  55. 55.
    Zhong J, Gan Y (2016) Detection of copy – move forgery using discrete analytical Fourier – Mellin transform. Nonlinear Dyn 84(1):189–202MathSciNetzbMATHCrossRefGoogle Scholar
  56. 56.
    Zhu Y, Shen X, Chen H (2016) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75(6):3221–3233CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jaypee University of Engineering and TechnologyGunaIndia

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