A passive forensic scheme for copy-move forgery based on superpixel segmentation and K-means clustering

  • Yong LiuEmail author
  • Hongxia Wang
  • Yi Chen
  • Hanzhou Wu
  • Huan Wang


Copy-move forgery is a commonly used operation for digital image. Most of the existing copy-move schemes designed to region duplication are block-based and keypoint-based. In general, block-based methods fail to handle geometric transformations. Though keypoint-based methods can handle geometric transformations, they have a poor detection effect on the smooth region. This has motivated us to propose an efficient copy-move forgery detection method, which is based on superpixel segmentation and cluster analysis to improve the detection accuracy due to some specified attacks in this paper. In the proposed method, K-means clustering technology is used to divide the superpixel of the image into complex regions and smooth regions. The clustering rule is based on the mean and standard deviation of the pixels, and the ratio of the feature points in the superpixel block, this clustering method can distinguish complex regions (non-smooth regions) and smooth regions. In complex regions, Scale-Invariant Feature Transform (SIFT) features are used to detect tampering. In smooth regions, the sector mask feature and RGB color feature are proposed to detect tampering. Filtering out error matching is applied to these two kinds of regions for the copy-move detection. Experimental results have shown that the proposed method can detect the tampering of complex regions and smooth regions and it indeed has the advantage in the detection accuracy compared with some related works when the test images are processed by blurring, adding noise, JPEG compression and rotation.


Copy-move forgery detection Image segmentation Cluster analysis Harris points Sector mean RGB color feature 



This work was supported by the Fundamental Research Funds for the Central Universities under the grant No. YJ201881 and Doctoral Innovation Fund Program of Southwest Jiaotong University.


  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2281CrossRefGoogle Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Bimbo AD, Serra G (2011) A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security 6(3):1099–1110CrossRefGoogle Scholar
  3. 3.
    Avola D, Bernardi M, Cinque L, Foresti GL, Massaroni C (2017) Adaptive bootstrapping management by keypoint clustering for background initialization. Pattern Recogn Lett 100:110–116CrossRefGoogle Scholar
  4. 4.
    Avola D, Bernardi M, Cinque L, Foresti GL, Massaroni C (2018) Combining keypoint clustering and neural background subtraction for real-time moving object detection by PTZ cameras. In: Proceedings of international conference on pattern recognition applications and methods, pp 638–645Google Scholar
  5. 5.
    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
  6. 6.
    Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Transactions on Information Forensics and Security 7(6):1841–1854CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Fridrich J, Soukalm D, Lukas J (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop, Cleveland, pp 19–23Google Scholar
  9. 9.
    Hamdi D, Iqbal F, Baker T, Shah B (2016) Multimedia file signature analysis for smartphone forensics. In: IEEE international conference international conference on developments in Esystems engineering, pp 130–137Google Scholar
  10. 10.
    Huang H, Guo W, Zhang Y (2009) Detection of copy-move forgery in digital images using SIFT algorithm. In: The workshop on computational intelligence and industrial application, vol 2, pp 272–276Google Scholar
  11. 11.
    Jing H, He X, Han Q, Abd El-Latif AA, Niu X (2014) Saliency detection based on integrated features. Neurocomputing 129:114–121CrossRefGoogle Scholar
  12. 12.
    Kang L, Cheng XP, Li K (2010) Copy-move forgery detection in digital image. In: Image and signal processing (CISP), vol 5, pp 2419–2421Google Scholar
  13. 13.
    Kumar S, Desai JV, Mukherjee S (2016) A fast Keypoint based hybrid method for copy move forgery detection. Ijcds Journal 4(2):91–99CrossRefGoogle Scholar
  14. 14.
    Li G, Wu Q, Tu D, Sun S (2007) A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: IEEE international conference on multimedia and expo, pp 1750–1753Google Scholar
  15. 15.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security 10(3):507–518CrossRefGoogle Scholar
  16. 16.
    Lian S, Kanellopoulos D (2009) Recent advances in multimedia information system security. Informatica 33:3–24MathSciNetGoogle Scholar
  17. 17.
    Liu Y, Wang HX, Wu HZ, Chen Y (2017) An efficient copy-move detection algorithm based on Superpixel segmentation and Harris key-points. In: International conference on cloud computing and security, pp 61–73Google Scholar
  18. 18.
    Macdermott A, Baker T, Shi Q, Shah B (2018) Iot forensics: challenges for the Ioa era. In: IFIP international conference on new technologies, mobility and security, pp 1–5Google Scholar
  19. 19.
    Mao YM, Lan MH, Wang YQ, Feng QS (2009) An improved corner detection method based on Harris. Computer technology and development 19(5):130–133Google Scholar
  20. 20.
    Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. In: Comput.sci.dartmouth College Private Ivy League Res.univ, p 646Google Scholar
  21. 21.
    Prakash CS, Kumar A, Maheshkar S, Maheshkar V (2018) An integrated method of copy-move and splicing for image forgery detection. Multimed Tools Appl 77(20):26939–26963CrossRefGoogle Scholar
  22. 22.
    Ryu S, Lee M, Lee H (2010) Detection of copy-rotate-move forgery using Zernike moments. Lect Notes Comput Sci 6387:51–65CrossRefGoogle Scholar
  23. 23.
    Sharif SA, Ali MA, Reqabi NA, Iqbal F, Baker T (2016) Magec: an image searching tool for detecting forged images in forensic investigation. In: IFIP international conference on new technologies, mobility and security, pp 1–6Google Scholar
  24. 24.
    Shi Z, Yu L, Abd El-Latif AA, Niu X (2012) Skeleton modulated topological perception map for rapid viewpoint selection. IEICE Trans Inf Syst E95D(10):2585–2588CrossRefGoogle Scholar
  25. 25.
    Tong X, Liu Y, Zhang M, Chen Y (2013) A novel chaos-based fragile watermarking for image tampering detection and self-recovery. Signal Process Image Commun 28:301–308CrossRefGoogle Scholar
  26. 26.
    Wang JW, Liu GJ, Zhang Z, Dai YW, Wang ZQ (2009) Fast and robust forensics for image region-duplication forgery. Acta Automat Sin 35(12):1488–1495CrossRefGoogle Scholar
  27. 27.
    Wang X, He G, Tang C, Han Y, Wang S (2016) Keypoints-based image passive forensics method for copy-move attacks. Int J Pattern Recognit Artif Intell 30(3):304–308MathSciNetGoogle Scholar
  28. 28.
    Wang H, Wang H, Sun X, Qian Q (2017) A passive authentication scheme for copy-move forgery based on package clustering algorithm. Multimed Tools Appl 76(10):12627–12644CrossRefGoogle Scholar
  29. 29.
    Wu HZ, Shi YQ, Wang HX, Zhou LN (2017) Separable reversible data hiding for encrypted palette images with color partitioning and flipping verification. IEEE Transactions on Circuits and Systems for Video Technology 27(8):1620–1631CrossRefGoogle Scholar
  30. 30.
    Zandi M, Mahmoudi-Aznaveh A, Mansouri A (2014) Adaptive matching for copy-move forgery detection. In: The workshop on information forensics and security, pp 119–124Google Scholar
  31. 31.
    Zandi M, Mahmoudi-Aznaveh A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. IEEE Transactions on Information Forensics and Security 11(11):2499–2512CrossRefGoogle Scholar
  32. 32.
    Zhang T, Abd El-Latif AA, Wang N, Li Q, Niu X (2012) A new image segmentation method via fusing NCut eigenvectors maps. In: International conference on digital image processing, vol 8334Google Scholar
  33. 33.
    Zhang T, Han Q, Abd El-Latif AA, Bai X, Niu X (2013) 2-D cartoon character detection based on scalable-shape context and Hough voting. Inf Technol J 12(12):2342–2349CrossRefGoogle Scholar
  34. 34.
    Zhou L, Wang D, Guo Y, Zhang J (2007) Blur detection of digital forgery using mathematical morphology. In: Proceedings of the 1st KES International symposium on agent and multi-agent systems. Technologies and applications. Springer-verlag, WroclawGoogle Scholar
  35. 35.
    Zhou Z, Wang Y, Wu QMJ, Yang CN, Sun X (2017) Effective and efficient global context verification for image copy detection. IEEE Transactions on Information Forensics and Security 12(1):48–63CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Yong Liu
    • 1
    Email author
  • Hongxia Wang
    • 2
  • Yi Chen
    • 3
  • Hanzhou Wu
    • 1
  • Huan Wang
    • 4
  1. 1.School of Communication and Information EngineeringShanghai UniversityShanghaiChina
  2. 2.College of CybersecuritySichuan UniversityChengduChina
  3. 3.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduPeople’s Republic of China
  4. 4.Guizhou University of Finance and EconomicsGuiyangPeople’s Republic of China

Personalised recommendations