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A comprehensive evaluation procedure for copy-move forgery detection methods: results from a systematic review

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Abstract

In the current age, the wide use of digital images has led to the manipulations of content that misrepresent information with malicious goals. This issue demands the requirement of digital image investigation to authenticate the source and certify the trustworthiness of images. One image manipulation technique is called copy-move forgery (CMF), which duplicates one or more regions in an image before it is pasted to another location within the same image. In this paper, a systematic review is conducted to assess the performance evaluation techniques implemented by current CMF detection methods’ approaches. Five research questions are generated to find and solve the related issues on the evaluation levels. At present, CMF detection performance is evaluated either through image-level, pixel-level, or both level evaluations. Image-level evaluation identifies an image either as forged or not while all images in pixel-level evaluation are treated as forged images to localise the CMF regions. The study shows that both image and pixel-level evaluations are dependent and must be incorporated together to ensure a fair evaluation is conducted. A comprehensive evaluation procedure that covers both evaluations is proposed as a guide to future research. The procedure is then examined with seven state-of-the-art CMF detection methods based on keypoint, block, and combination approaches using three available CMF datasets that consist of multiple CMF attacks. The results are measured using multiple F-score values: image scores, pixel scores, and a multiplication of both of these to get the overall percentages of the detection. The results show that the block-based approach is able to obtain the highest percentages of detection in almost all cases of attacks in CMF.

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References

  1. Abdalla Y, Iqbal MT, Shehata M (2019) Convolutional neural network for copy-move forgery detection. Symmetry (Basel) 11(10):1–17. https://doi.org/10.3390/sym11101280

    Article  Google Scholar 

  2. Agarwal R, Verma OP (2020) An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimed Tools Appl 79(11–12):7355–7376. https://doi.org/10.1007/s11042-019-08495-z

    Article  Google Scholar 

  3. 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):284–295. https://doi.org/10.1016/j.forsciint.2013.05.027

    Article  Google Scholar 

  4. Al-Qershi OM, Khoo BE (2018) Evaluation of copy-move forgery detection: datasets and evaluation metrics. Multimed Tools Appl 77(24):31807–31833. https://doi.org/10.1007/s11042-018-6201-4

    Article  Google Scholar 

  5. Al-Qershi OM, Khoo BE (2019) Enhanced block-based copy-move forgery detection using k-means clustering. Multidimens Syst Signal Process 30(4):1671–1695. https://doi.org/10.1007/s11045-018-0624-y

    Article  MATH  Google Scholar 

  6. 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):1099–1110

    Article  Google Scholar 

  7. Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (Jul. 2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28(6):659–669. https://doi.org/10.1016/j.image.2013.03.006

    Article  Google Scholar 

  8. 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

    Article  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. https://doi.org/10.1016/j.ins.2016.01.061

    Article  Google Scholar 

  10. Bo X, Junwen W, Guangjie L, Yuewei D (2010) Image copy-move forgery detection based on SURF. In: International conference on multimedia information networking and security, pp 889–892. https://doi.org/10.1109/MINES.2010.189

    Chapter  Google Scholar 

  11. 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):1759–1770. https://doi.org/10.1016/j.sigpro.2011.01.022

    Article  MATH  Google Scholar 

  12. Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1–3):33–43. https://doi.org/10.1016/j.forsciint.2011.07.015

    Article  Google Scholar 

  13. Carkir S, Cetin AE (2010) Two-dimensional mellin and mel-cepstrum for image feature extraction. In: Proceedings of the 25th International Symposium on Computer and Information Sciences, pp 271–276

    Google Scholar 

  14. Chen CC, Lu WY, Chou CH (2019) Rotational copy-move forgery detection using SIFT and region growing strategies. Multimed Tools Appl 78(13):18293–18308. https://doi.org/10.1007/s11042-019-7165-8

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. Cozzolino D, Poggi G, Verdoliva L (2014) Copy-Move Forgery Detection Based On Patchmatch. In: IEEE International Conference on Image Processing, pp 5247–5251

    Google Scholar 

  17. 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 

  18. Davarzani R, Yaghmaie K, Mozaffari S, Tapak M (2013) Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci Int 231(1–3):61–72. https://doi.org/10.1016/j.forsciint.2013.04.023

    Article  Google Scholar 

  19. Dixit R, Naskar R (2017) Review, analysis and parameterisation of techniques for copy-move forgery detection in digital images. IET Image Process 11(9):746–759. https://doi.org/10.1049/iet-ipr.2016.0322

    Article  Google Scholar 

  20. Elaskily MA, Elnemr HA, Sedik A, Dessouky MM, el Banby GM, Elshakankiry OA, Khalaf AAM, Aslan HK, Faragallah OS, Abd el-Samie FE (2020) A novel deep learning framework for copy-moveforgery detection in images. Multimed Tools Appl 79(27–28):19167–19192. https://doi.org/10.1007/s11042-020-08751-7

    Article  Google Scholar 

  21. 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. https://doi.org/10.1007/s11042-015-2872-2

    Article  Google Scholar 

  22. Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. Int J Comput Sci Issues 3:652–663. https://doi.org/10.1109/PACIIA.2008.240

    Article  Google Scholar 

  23. Gani G, Qadir F (2020) A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata. J Inf Secur Appl 54:102510. https://doi.org/10.1016/j.jisa.2020.102510

    Article  Google Scholar 

  24. Goel N, Kaur S, Bala R (2021) Dual branch convolutional neural network for copy move forgery detection. IET Image Process 15(3):656–665. https://doi.org/10.1049/ipr2.12051

    Article  Google Scholar 

  25. Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pp 272–276. https://doi.org/10.1109/PACIIA.2008.240

    Chapter  Google Scholar 

  26. 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):178–184. https://doi.org/10.1016/j.forsciint.2010.08.001

    Article  Google Scholar 

  27. Jing D, Wei W (2011) CASIA tampered image detection evaluation (TIDE) database. . http://forensics.idealtest.org/casiav2/. Accessed 28 Apr 2015

  28. Kim S, In-So K (2002) Probabilistic model-based object recognition using local Zemike moments. In: IAPR workshop on machine vision applications, pp 11–14

    Google Scholar 

  29. Langille A, Gong M (2006) An efficient match-based duplication detection algorithm. In: 3rd Canadian Conference on Computer and Robot Vision (CRV’06), pp 64–64. https://doi.org/10.1109/CRV.2006.9

    Chapter  Google Scholar 

  30. Li Y, Zhou J (2019) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inf Forensics Secur 14(5):1307–1322. https://doi.org/10.1109/TIFS.2018.2876837

    Article  Google Scholar 

  31. 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. https://doi.org/10.1109/TIFS.2014.2381872

    Article  Google Scholar 

  32. Liao X, Yin J, Chen M, Qin Z (2020) Adaptive payload distribution in multiple images steganography based on image texture features. IEEE Trans. Dependable Secur. Comput XX(XX). https://doi.org/10.1109/TDSC.2020.3004708

  33. Liao X, Yu Y, Li B, Li Z, Qin Z (2020) A new payload partition strategy in color image steganography. IEEE Trans Circuits Syst Video Technol 30(3):685–696. https://doi.org/10.1109/TCSVT.2019.2896270

    Article  Google Scholar 

  34. Liao X, Li K, Zhu X, Liu KJR (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE J Sel Top Signal Process 14(5):955–968. https://doi.org/10.1109/JSTSP.2020.3002391

    Article  Google Scholar 

  35. Lynch G, Shih FY, Liao H-YM (2013) An efficient expanding block algorithm for image copy-move forgery detection. Inf Sci (Ny) 239:253–265. https://doi.org/10.1016/j.ins.2013.03.028

    Article  Google Scholar 

  36. Mahdian B, Saic S (2007) Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci Int 171:180–189. https://doi.org/10.1016/j.forsciint.2006.11.002

    Article  Google Scholar 

  37. Meena KB, Tyagi V (2020) A copy-move image forgery detection technique based on tetrolet transform. J Inf Secur Appl. 52:102481. https://doi.org/10.1016/j.jisa.2020.102481

    Article  Google Scholar 

  38. Mishra P, Mishra N, Sharma S, Patel R (2008) Region duplication forgery detection technique based on SURF and HAC. Sci World J 2013(July):2013

    Google Scholar 

  39. Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using Undecimated dyadic wavelet transform. Digit Investig 9(1):49–57. https://doi.org/10.1016/j.diin.2012.04.004

    Article  Google Scholar 

  40. Myna AN, Venkateshmurthy MG, Patil CG (2008) Detection of region duplication forgery in digital images using wavelets and log-polar mapping. In: Proceedings - International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2007, vol 3, pp 371–377. https://doi.org/10.1109/ICCIMA.2007.161

    Chapter  Google Scholar 

  41. Niyishaka P, Bhagvati C (2020) Copy-move forgery detection using image blobs and BRISK feature. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09225-6

  42. Park JY, Kang TA, Moon YH, Eom IK (2020) Copy-move forgery detection using scale invariant. Symmetry (Basel). 12(492):1–16

    Google Scholar 

  43. Prakash CS, Panzade PP, Om H, Maheshkar S (2019) Detection of copy-move forgery using AKAZE and SIFT keypoint extraction. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7629-x

  44. 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

    Article  Google Scholar 

  45. Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using zernike moments. In: 12th international conference, vol 6387, pp 51–65. https://doi.org/10.1007/978-3-642-16435-4_5

    Chapter  Google Scholar 

  46. 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. https://doi.org/10.1109/TIFS.2013.2272377

    Article  Google Scholar 

  47. 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. https://doi.org/10.1016/j.jvcir.2015.01.016

    Article  Google Scholar 

  48. Somayeh S, Hamid AJ, Wong K, Uliyan D, Dadkhah S (2017) Keypoint based authentication and localization of copy-move forgery In digital image. Malaysian J Comput Sci 30(2):117–133

    Article  Google Scholar 

  49. Tahaoglu G, Ulutas G, Ustubioglu B, Nabiyev VV (2021) Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique. Multimed Tools Appl 80(15):23419–23456. https://doi.org/10.1007/s11042-020-10241-9

    Article  Google Scholar 

  50. Teerakanok S, Uehara T (2019) Copy-move forgery detection: a state-of-the-art technical review and analysis. IEEE Access 7:40550–40568. https://doi.org/10.1109/ACCESS.2019.2907316

    Article  Google Scholar 

  51. 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. https://doi.org/10.1016/j.aeue.2016.05.005

    Article  Google Scholar 

  52. Vaishnavi D, Subashini TS (2019) Application of local invariant symmetry features to detect and localize image copy move forgeries. J Inf Secur Appl 44:23–31. https://doi.org/10.1016/j.jisa.2018.11.001

    Article  Google Scholar 

  53. Wang XY, Wang C, Wang L, Jiao LX, Yang HY, Niu PP (2020) A fast and high accurate image copy-move forgery detection approach. Multidimens Syst Signal Process 31(3):857–883. https://doi.org/10.1007/s11045-019-00688-x

    Article  MATH  Google Scholar 

  54. Wang Y, Kang X, Chen Y (2020) Robust and accurate detection of image copy-move forgery using PCET-SVD and histogram of block similarity measures. J Inf Secur Appl 54:102536. https://doi.org/10.1016/j.jisa.2020.102536

    Article  Google Scholar 

  55. Warif NBA, Wahab AWA, Idris MYI, Ramli R, Salleh R, Shamshirband S, Choo KKR (2016) Copy-move forgery detection: survey, challenges and future directions. J Netw Comput Appl 75:259–278. https://doi.org/10.1016/j.jnca.2016.09.008

    Article  Google Scholar 

  56. Warif NBA, Wahab AWA, Idris MYI, Salleh R, Othman F (2017) SIFT-symmetry: a robust detection method for copy-move forgery with reflection attack. J Vis Commun Image Represent 46:219–232. https://doi.org/10.1016/j.jvcir.2017.04.004

    Article  Google Scholar 

  57. Warif NBA, Idris MYI, Abdul Wahab AW, Salleh R, Ismail A (2019) CMF-iteMS: an automatic threshold selection for detection of copy-move forgery. Forensic Sci Int 295:83–99. https://doi.org/10.1016/j.forsciint.2018.12.004

    Article  Google Scholar 

  58. Yu L, Han Q, Niu X (2016) Feature point-based copy-move forgery detection: covering the non-textured areas. Multimed Tools Appl 75(2):1159–1176

    Article  Google Scholar 

  59. 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):1–2512. https://doi.org/10.1109/TIFS.2016.2585118

    Article  Google Scholar 

  60. 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):158–166. https://doi.org/10.1016/j.forsciint.2013.09.013

    Article  Google Scholar 

  61. Zheng J, Liu Y, Ren J, Zhu T, Yan Y, Yang H (2016) Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimens Syst Signal Process. 27(4):989–1005. https://doi.org/10.1007/s11045-016-0416-1

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhu Y, Chen C, Yan G, Guo Y, Dong Y (2020) AR-net: adaptive attention and residual refinement network for copy-move forgery detection. IEEE Trans Ind Informatics 16(10):6714–6723. https://doi.org/10.1109/TII.2020.2982705

    Article  Google Scholar 

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Acknowledgements

This research was supported by Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2020/ICT04/UTHM/02/1).

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Authors and Affiliations

  1. Center of Information Security Research, Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nor Bakiah Abd Warif

  2. Department of Computer, System, & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Mohd. Yamani Idna Idris, Ainuddin Wahid Abdul Wahab & Rosli Salleh

  3. Department of Computer System and Networking, Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Nor-Syahidatul N. Ismail

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  1. Nor Bakiah Abd Warif
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Warif, N.B.A., Idris, M.Y.I., Wahab, A.W.A. et al. A comprehensive evaluation procedure for copy-move forgery detection methods: results from a systematic review. Multimed Tools Appl 81, 15171–15203 (2022). https://doi.org/10.1007/s11042-022-12010-2

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