Multimedia Tools and Applications

, Volume 78, Issue 16, pp 23535–23558 | Cite as

Detection of copy-move forgery using AKAZE and SIFT keypoint extraction

  • Choudhary Shyam PrakashEmail author
  • Prajwal Pralhad Panzade
  • Hari Om
  • Sushila Maheshkar


Digital image manipulation techniques are becoming increasingly sophisticated and widespread. Copy-move forgery is one of the frequently used manipulation techniques. In this paper, we propose a keypoint based copy-move forgery detection (CMFD) technique, which is a combination of accelerated KAZE (AKAZE) and scale invariant feature transform (SIFT) features. By using AKZAE and SIFT, a significant number of keypoints are extracted even in a smooth region to detect the manipulated regions efficiently. After formation of the mixed keypoints, the g2NN is used for matching process to locate the duplicated regions. The experimental results show that the proposed method can detect the duplicated regions even if the image is post-processed with scaling, rotation, noise and JPEG compression operations. To validate the robustness and effectiveness of the proposed method, a statistical analysis is performed using the ANOVA method.


Image forensics Copy-move forgery Duplicated region detection SIFT AKAZE 



  1. 1.
    Alcantarilla PF, Solutions T (2011) Fast explicit diffusion for accelerated features in nonlinear scale spaces. IEEE Trans Patt Anal Mach Intell 34(7):1281–1298Google Scholar
  2. 2.
    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
  3. 3.
    Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf Forensic Secur 6(3):1099–1110CrossRefGoogle Scholar
  4. 4.
    Ammour N, Alhichri H, Bazi Y, Benjdira B, Alajlan N, Zuair M (2017) Deep learning approach for car detection in uav imagery. Remote Sens 9(4):312CrossRefGoogle Scholar
  5. 5.
    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):281–307CrossRefGoogle Scholar
  6. 6.
    Bakator M, Radosav D (2018) Deep learning and medical diagnosis: A review of literature. Multimodal Technol Interact 2(3):47CrossRefGoogle Scholar
  7. 7.
    Barnes C, Shechtman E, Finkelstein A, Goldman DB (2009) Patchmatch: A randomized correspondence algorithm for structural image editing. ACM Trans Graph 28(3):24–1CrossRefGoogle Scholar
  8. 8.
    Bay H, Tuytelaars T, Gool LV (2006) Surf: Speeded up robust features. Computer vision? ECCV 2006, pp 404–417Google Scholar
  9. 9.
    Beis JS, Lowe DG (1997) Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: 1997. Proceedings., 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 1000–1006Google Scholar
  10. 10.
    Bo X, Junwen W, Guangjie L, Yuewei D (2010) Image copy-move forgery detection based on surf. In: 2010 international conference on Multimedia Information Networking and Security (MINES). IEEE, pp 889–892Google Scholar
  11. 11.
    Bravo-Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. . IEEE, 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 Forensic Secur 7(6):1841–1854CrossRefGoogle Scholar
  14. 14.
    Cozzolino D, Poggi G, Verdoliva L (2014) Copy-move forgery detection based on patchmatch. In: IEEE International Conference on Image Processing (ICIP). IEEE, pp 5312–5316Google Scholar
  15. 15.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Trans Inf Forensic 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.
    Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer Series in Statistics, New York, vol 1Google Scholar
  18. 18.
    Grewenig S, Weickert J, Bruhn A (2010) From box filtering to fast explicit diffusion. In: DAGM-Symposium. Springer, pp 533–542Google Scholar
  19. 19.
    Hayat K, Qazi T (2017) Forgery detection in digital images via discrete wavelet and discrete cosine transforms. Comput Electr Eng 62:448–458CrossRefGoogle Scholar
  20. 20.
    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
  21. 21.
    Jessica Fridrich A, David Soukal B, Jan Lukááš A (2003) Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. CiteseerGoogle Scholar
  22. 22.
    Li G, Wu Q, Tu D (2007) Shaojie Sun. A sorted neighborhood approach for detecting duplicated regions in image forgeries based on dwt and svd. In: International Conference on Multimedia and Expo IEEE. IEEE, pp 1750–1753Google Scholar
  23. 23.
    Li L, Li S, Zhu H, Chu S-C, Roddick JF, Pan J-S (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inf Hiding Multimed Signal Process 4(1):46–56Google Scholar
  24. 24.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensic Secur 10(3):507–518CrossRefGoogle Scholar
  25. 25.
    Li S, Zhang Z, Li B, Li C (2018) Multiscale rotated bounding box-based deep learning method for detecting ship targets in remote sensing images. Sensors 18 (8):2702CrossRefGoogle Scholar
  26. 26.
    Liu L, Wiliem A, Chen S, Lovell BC (2014) Automatic image attribute selection for zero-shot learning of object categories. In: 2014 22nd International Conference on Pattern Recognition. IEEE, pp 2619–2624Google Scholar
  27. 27.
    Liu L, Wiliem A, Chen S, Lovell BC (2017) What is the best way for extracting meaningful attributes from pictures? Pattern Recogn 64:314–326CrossRefGoogle Scholar
  28. 28.
    Liu L, Nie F, Wiliem A, Li Z, Zhang T, Lovell BC (2018) Multi-modal joint clustering with application for unsupervised attribute discovery. IEEE Trans Image Process 27(9):4345–4356MathSciNetCrossRefGoogle Scholar
  29. 29.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  30. 30.
    Luo W, Huang J, Qiu G (2006) Robust detection of region-duplication forgery in digital image. In: 2006. 18th International Conference on Pattern Recognition ICPR. IEEE, vol 4, pp 746–749Google Scholar
  31. 31.
    Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2):180–189CrossRefGoogle Scholar
  32. 32.
    Muller KE, Fetterman BA (2002) Regression and ANOVA: an integrated approach using SAS software. SAS InstituteGoogle Scholar
  33. 33.
    Pan X, Lyu S (2010) Detecting image region duplication using sift features. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP). IEEE, pp 1706–1709Google Scholar
  34. 34.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensic Secur 5(4):857–867CrossRefGoogle Scholar
  35. 35.
    Panzade PP, Prakash CS, Maheshkar S (2016) Copy-move forgery detection by using hsv preprocessing and keypoint extraction. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, pp 264–269Google Scholar
  36. 36.
    Popescu A C, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. department computer science, dartmouth college technology report tr2004-515Google Scholar
  37. 37.
    Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, pp 1–6Google Scholar
  38. 38.
    Ryu S-J, Lee M-J, Lee H-K (2010) Detection of copy-rotate-move forgery using zernike moments. In Information hiding. Springer, vol 6387, pp 51–65Google Scholar
  39. 39.
    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 Forensic Secur 8(8):1355–1370CrossRefGoogle Scholar
  40. 40.
    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
  41. 41.
    Toldo R, Fusiello A (2008) Robust multiple structures estimation with j-linkage. Computer Vision? ECCV 2008, pp 537–547Google Scholar
  42. 42.
    Wang C, Zhang Z, Zhou X (2018) An image copy-move forgery detection scheme based on a-kaze and surf features. Symmetry 10(12):706CrossRefGoogle Scholar
  43. 43.
    Weickert J, Grewenig S, Schroers C, Bruhn A Cyclic schemes for pde-based image analysis. International Journal of Computer Vision 118(3):275–299Google Scholar
  44. 44.
    Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83CrossRefGoogle Scholar
  45. 45.
    Zandi M, Mahmoudi-Aznaveh A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans Inf Forensic Secur 11(11):2499–2512CrossRefGoogle Scholar
  46. 46.
    Zhang T, Liu L, Zhao K, Wiliem A, Hemson G, Lovell B (2018) Omni-supervised joint detection and pose estimation for wild animals. Pattern Recognition LettersGoogle Scholar
  47. 47.
    Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyIndian Institute of Technology (Indian School of Mines)DhanbadIndia

Personalised recommendations