Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31807–31833 | Cite as

Evaluation of copy-move forgery detection: datasets and evaluation metrics

  • Osamah M. Al-Qershi
  • Bee Ee KhooEmail author


Creating copy-move forgery became even easier using a wide range of software and platforms. Many algorithms have been proposed to solve the problem, but each one of those algorithms has its own drawbacks. Researchers face many challenges in developing copy-move detection algorithms, and in this paper, we focus on two challenges. The first is the benchmark dataset, and the second involves evaluation metrics. In this paper, we investigate the available copy-move datasets and their advantages and disadvantages. In addition, we discuss the different metrics that have been used by researchers to evaluate the copy-move forgery detection (CMFD) algorithms. On that basis, we suggest the standard specifications of the appropriate copy-move dataset and the metrics that should be used to evaluate the detection algorithms. The findings of this paper will help researchers evaluate their algorithms effectively and fairly essential for developing reliable algorithms.


Copy-move Digital image forensics Image forgery 



The authors would like to acknowledge the financial assistance provided by the Ministry of Education Malaysia through FRGS grant number 203/PELECT/6071305.


  1. 1.
    Al-Qershi OM, Khoo BE (Sep. 2013) Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci Int 231(1–3):284–295CrossRefGoogle Scholar
  2. 2.
    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 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.
    Amtullah S, Koul DA (2014) Passive image forensic method to detect copy move forgery in digital images. IOSR J Comput Eng 16(2):96–104CrossRefGoogle Scholar
  5. 5.
    Ardizzone E, Bruno A, Mazzola G (2010) Copy-move forgery detection via texture description. In: Proceedings of the 2010 ACM Workshop on Multimedia in Forensics, Security and Intelligence,(MiFor’10), pp. 59–64Google Scholar
  6. 6.
    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
  7. 7.
    Bakiah N et al (2016) Copy-move forgery detection : survey, challenges and future directions. J Netw Comput Appl 75:259–278CrossRefGoogle Scholar
  8. 8.
    Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10:226–245CrossRefGoogle Scholar
  9. 9.
    Bravo-Solorio S, Nandi AK (2011) Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Process 91(8):1759–1770CrossRefGoogle Scholar
  10. 10.
    Brownlee J (2015) Metrics To Evaluate Machine Learning Algorithms in Python. Machine Learning Mastery. [Online]. Available: Accessed 06 Oct 2016
  11. 11.
    Cao Y, Gao T, Fan L, Yang Q (2011) A robust detection algorithm for region duplication in digital images. Int J Digit Content Technol its Appl 5(6):95–103CrossRefGoogle Scholar
  12. 12.
    Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 2014(1–3):33–43CrossRefGoogle Scholar
  13. 13.
    Chihaoui T, Bourouis S, Hamrouni K (2014) Copy move image forgery detection based on SIFT descriptors and SVD matching. In: Proceedings of the International Conference on Advanced Technologies for Signal and Image Processing, pp 125–129Google Scholar
  14. 14.
    Christlein V, Riess C, Jordan J, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854CrossRefGoogle Scholar
  15. 15.
    Chubb M (2014) In-depth Social Media picture sizes. Available: Accessed 23 Sep 2016
  16. 16.
    Cozzolino D, Poggi G, Verdoliva L (2014) Copy-move forgery detection based on patchmatch. In: The International Conference on Image Processing (ICIP), pp 5247–5251Google Scholar
  17. 17.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy – move forgery detection. Ieee Trans Inf Forensics Secur 10(11):2284–2297CrossRefGoogle Scholar
  18. 18.
    Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (Sep. 2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231(1–3):61–72CrossRefGoogle Scholar
  19. 19.
    Doyoddorj M, Rhee K-H (2013) Robust copy-move forgery detection based on dual-transform. In: Proceedings of the 5th International Conference on Digital Forensics and Cyber Crime, pp. 3–16Google Scholar
  20. 20.
    Emam M, Han Q, Niu X (2015) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 75(18):11513–11527CrossRefGoogle Scholar
  21. 21.
    Fadl SM, Semary NA (2015) A proposed accelerated image copy-move forgery detection. In: Proceedings of the IEEE Visual Communications and Image Processing Conference, VCIP, pp. 253–257Google Scholar
  22. 22.
    Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Proceedings of DFRWS 2003. ClevelandGoogle Scholar
  23. 23.
    Hashmi MF, Anand V, Keskar AG (2014) Copy-move image forgery detection using an efficient and robust method combining un-decimated wavelet transform and scale invariant feature transform. AASRI Procedia 9:84–91CrossRefGoogle Scholar
  24. 24.
    Hashmi MF, Hambarde AR, Keskar AG (2014) Copy move forgery detection using DWT and SIFT features. In: Proceedings of the international conference on intelligent systems design and applications, ISDA, pp 188–193Google Scholar
  25. 25.
    He H, Huang X, Kuang J (2013) Exposing copy-move forgeries based on a dimension-reduced Sift method. Inf Technol J 12(14):2975–2979CrossRefGoogle Scholar
  26. 26.
    Hsu H-C, Wang M-S (2012) Detection of copy-move forgery image using Gabor descriptor. In: Anti-counterfeiting, security and identification (ASID), 2012 international conference on, pp 1–4Google Scholar
  27. 27.
    Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206(1–3):178–184CrossRefGoogle Scholar
  28. 28.
    Kirchner M, Sch P (2015) Thinking beyond the block: block matching for copy – move forgery detection revisited. In: Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, vol. 9409, pp. 1–12Google Scholar
  29. 29.
    Kohavi R, Provost F (1998) Glossary of terms. Mach Learn 30(2/3):271–274CrossRefGoogle Scholar
  30. 30.
    Kulkarni VS, Chavan YV (2014) Comparison of methods for detection of copy-move forgery in digital images. Spvryan’s Int J Eng Sci Technol 1(1):1–6CrossRefGoogle Scholar
  31. 31.
    Kumar S, Desai J, Mukherjee S (2013) A fast DCT based method for copy move forgery detection. In: IEEE Second International Conference on Image Information Processing, pp 649–654Google Scholar
  32. 32.
    J. Lee, K. Chang, C. Chang, and C. Cheng (2014) Image copy-move forgery detection based on HOG. In: Proceedings of the 27th IPPR conference on computer vision, graphics, and image processing, pp 1–5Google Scholar
  33. 33.
    Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1–3):59–67CrossRefGoogle Scholar
  34. 34.
    L. Li, S. Li, and J. Wang (2012) Copy-move forgery detection based on PHT. In: Proceedings of the 2012 World Congress on Information and Communication Technologies, WICT 2012, pp. 1061–1065Google Scholar
  35. 35.
    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
  36. 36.
    Liu F, Feng H (2014) An efficient algorithm for image copy-move forgery detection based on DWT and SVD. Int J Secur Its Appl 8(5):377–390Google Scholar
  37. 37.
    Liu F, Feng H (2014) A novel algorithm for image copy-move forgery detection and localization based on SVD and projection data. Int J Multimed Ubiquitous Eng 9(9):189–200MathSciNetCrossRefGoogle Scholar
  38. 38.
    Liu G, Wang J, Lian S, Wang Z (2010) A passive image authentication scheme for detecting region-duplication forgery with rotation. J Netw Comput Appl 34(5):1557–1565CrossRefGoogle Scholar
  39. 39.
    Liu L, Ni R, Zhao Y, Li S (2014) Improved SIFT-based copy-move detection using BFSN clustering and CFA features. In: Proceedings of the 10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 626–629Google Scholar
  40. 40.
    Lynch G, Shih FY, Liao H-YM (Aug. 2013) An efficient expanding block algorithm for image copy-move forgery detection. Inf Sci (Ny) 239(0):253–265CrossRefGoogle Scholar
  41. 41.
    Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25(6):389–399CrossRefGoogle Scholar
  42. 42.
    Maind RA, Khade A, Chitre DK (2014) Image copy move forgery detection using block representing method. Int J Soft Comput Eng 4(2):49–53Google Scholar
  43. 43.
    Malviya AV, Ladhake SA (2015) Copy move forgery detection using low complexity feature extraction. In: Proceedings of the IEEE UP Section Conference on Electrical Computer and Electronics, pp. 1–5Google Scholar
  44. 44.
    Manning CD, Raghavan P, Schütze H (2009) Chapter 8: evaluation in information retrieval. In: Introduction to Information Retrieval, no. c, pp. 151–175Google Scholar
  45. 45.
    Manu VT, Mehtre BM (2016) Detection of Copy-Move Forgery in Images Using Segmentation and SURF. Proceedings of the 2nd International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS) 425:645–654CrossRefGoogle Scholar
  46. 46.
    Metz CE (1978) Basic principles of ROC analysis. Semin Nucl Med 8(4):283–298CrossRefGoogle Scholar
  47. 47.
    Muhammad G, Hussain M, Khawaji K, Bebis G (2011) Blind copy move image forgery detection using dyadic undecimated wavelet transform. In: 17th DSP 2011 International Conference on Digital Signal Processing, ProceedingsGoogle Scholar
  48. 48.
    Oommen RS, Jayamohan M, Sruthy S (2016) Scale Invariant Detection of Copy-Move Forgery Using Fractal Dimension and Singular Values. Proceedings of the 2nd International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS) 425:559–570CrossRefGoogle Scholar
  49. 49.
    Park C-S, Kim C, Lee J, Kwon G-R (2016) Rotation and scale invariant upsampled log-polar fourier descriptor for copy-move forgery detection. Multimed Tools Appl 1–19Google Scholar
  50. 50.
    Powers DMW (2007) Evaluation: from precision, recall and F-factor to ROC, Informedness, Markedness & Correlation. J Mach Learn Technol 2(1):37–63Google Scholar
  51. 51.
    Pun C, Member S, Yuan X, Bi X (2015) Image forgery detection using adaptive over segmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1–12CrossRefGoogle Scholar
  52. 52.
    Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162CrossRefGoogle Scholar
  53. 53.
    Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using zernike moments. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6387 LNCS, pp. 51–65CrossRefGoogle Scholar
  54. 54.
    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 Forensics Secur 8(8):1355–1370CrossRefGoogle Scholar
  55. 55.
    Satapathy SC, Biswal BN, Udgata SK, Mandal JK (2014) Passive copy move forgery detection using SURF, HOG and SIFT Features. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), vol. 327, pp. 659–666Google Scholar
  56. 56.
    Sharma S, Ghanekar U (2015) A rotationally invariant texture descriptor to detect copy move forgery in medical images. In: Proceedings of the IEEE International Conference on Computational Intelligence & Communication Technology, pp. 795–798Google Scholar
  57. 57.
    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
  58. 58.
    Singh VK, Tripathi RC (2015) Fast rotation invariant detection of region duplication attacks even on uniform background containing digital images. Procedia Comput Sci 54:772–780CrossRefGoogle Scholar
  59. 59.
    Sunil K, Jagan D, Shaktidev M (2014) DCT-PCA based method for copy-move forgery detection. Advances in Intelligent Systems and Computing 249:577–583CrossRefGoogle Scholar
  60. 60.
    Thajeel SA, Sulong G (2015) A Novel Approach for detection of copy move forgery using completed robust local binary pattern. J Inf Hiding Multimed Signal Process 6(2):351–364Google Scholar
  61. 61.
    Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1521–1528Google Scholar
  62. 62.
    Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD - new database for copy-move forgery detection. In: Proceedings of 55th international symposium ELMAR, pp 49–54Google Scholar
  63. 63.
    Tralic D, Rosin PL, Sun X, 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
  64. 64.
    Tralic D, Grgic S, Sun X, Rosin PL (2015) Combining cellular automata and local binary patterns for copy-move forgery detection. Multimed Tools Appl, pp. 1–23Google Scholar
  65. 65.
    Uliyan DM, Jalab H, Abdul Wahab AW (2015) Copy move image forgery detection using hessian and center symmetric local binary pattern. In: Proceedings of the IEEE Conference on Open Systems ICOS, pp 7–11Google Scholar
  66. 66.
    Uliyan D, Jalab H, Abdul Wahab A, Sadeghi S (2016) Image region duplication forgery detection based on angular radial partitioning and Harris key-points. Symmetry (Basel) 8(7):62MathSciNetCrossRefGoogle Scholar
  67. 67.
    Ulysses JN, Conci A (2010) Measuring similarity in medical registration. In: International Conference on Systems, Signals and Image Processing, p. 4Google Scholar
  68. 68.
    Ustubioglu B, Ulutas G, Ulutas M, Nabiyev V (2016) A new copy move forgery detection technique with automatic threshold determination. AEU Int J Electron Commun 70(8):1076–1087CrossRefGoogle Scholar
  69. 69.
    Wang T, Tang J, Zhao W, Xu Q, Luo B (2012) Blind detection of copy-move forgery based on multi-scale autoconvolution invariants. Communications in Computer and Information Science 321: 438–446. CCISGoogle Scholar
  70. 70.
    Wang T, Tang J, Luo B (2013) Blind detection of region duplication forgery by merging blur and affine moment invariants. In: Proceedings of Seventh International Conference on Image and Graphics, pp. 258–264Google Scholar
  71. 71.
    Wen B, Ye Z, Ng RST-T, Shen X, Winkler S (2016) COVERAGE – a novel database for copy-move forgery detection,” In: Proceedings of the IEEE international conference on image processing (ICIP), pp. 161–165Google Scholar
  72. 72.
    What Size Should Online Images Be Uploaded to Avoid Theft?. 2014. [Online]. Available: [Accessed: 23-Sep-2016]
  73. 73.
    Yang J, Ran P, Xiao D, Tan J (2013) Digital image forgery forensics by using undecimated dyadic wavelet transform and Zernike moments. J Comput Inf Syst 9(16):6399–6408Google Scholar
  74. 74.
    Yu L, Han Q, Niu X (2014) Copy-rotation-move forgery detection using the MROGH descriptor. In: Proceedings of the IEEE international conference on cloud engineering, IC2E, pp 510–513Google Scholar
  75. 75.
    Yu L, Han Q, Niu X (2016) Feature point-based copy-move forgery detection: covering the non-textured areas. Multimed Tools Appl 75(2):1159–1176CrossRefGoogle Scholar
  76. 76.
    Zhang J, Feng Z, Su Y (2008) A new approach for detecting copy-move forgery in digital images. In: Communication systems, 2008. ICCS 2008. 11th IEEE Singapore international conference on, pp. 362–366Google Scholar
  77. 77.
    Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233(1–3):158–166CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical & Electronic EngineeringUniversiti Sains MalaysiaNibong TebalMalaysia

Personalised recommendations