Skip to main content
SpringerLink
Log in

Region duplication detection in digital images based on Centroid Linkage Clustering of key–points and graph similarity matching

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Region duplication or copy–move forgery is an attack in which a region of an image is copied and pasted onto another location of the same image. In the recent state–of–the–art, a number of key–point based methods have been proposed for copy–move forgery detection in digital images. Though the problems of re–scaling and rotation in region duplication, have been sufficiently investigated using key–point based methods, post-processing based attacks such as flip, blur, brightness and noise, remain an open challenge in this field. In this paper, we address the problem of copy–move forgery detection in images, plus aim to identify copied regions, having undergone different geometric (such as rotation, re–scale), and post–processing attacks (such as Gaussian noise, blurring and brightness adjustment). In the proposed algorithm we introduce a region based key–point selection concept, which is considerably more discriminative than single SIFT key–point extraction. In this work, we apply Centroid Linkage Clustering, to identify duplicated regions in an image, from matched key-points. Also, we introduce a Graph Similarity Matching algorithm, to optimize false matches. Our experimental results demonstrate the efficiency of the proposed method in terms of forgery detection and localization efficiency, for a wide range of geometric and post-processing based attacks in region duplication.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. 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 

  2. Amerini I, Ballan L, Caldelli R, Bimbo AD, Tongo LD, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process Image Commun 28(6):659–669

    Article  Google Scholar 

  3. Andrea B, Parmiggian F (1996) Single linkage region growing algorithms based on the vector degree of match. IEEE Trans Geosci Remote Sens 34(1):137–148

    Article  Google Scholar 

  4. Ardizzone E, Bruno A, Mazzola G (2015) Copy–move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–94

    Article  Google Scholar 

  5. Bayram S, Sencar HT, Memon TN (2009) An efficient and robust method for detecting copy–move forgery. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2009, pp 1053–1056

  6. Blashfield RK (1976) Mixture model tests of cluster analysis: accuracy of four agglomerative hierarchical methods. Psychol Bull 88(3):377

    Article  Google Scholar 

  7. 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

  8. Caldelli R, Amerini I, Ballan L, Serra G, Barni M, Costanzo A (2012) On the effectiveness of local warping against SIFT–based copy-move detection. In: Communications control and signal processing (ISCCSP), pp 1–5

  9. Coeurjolly D, Klette R (2004) A comparative evaluation of length estimators of digital curves. IEEE Trans Pattern Anal Mach Intell 26(2):252–258

    Article  Google Scholar 

  10. Danielsson PE (1980) Euclidean distance mapping. Comput Graphics Image Process 3:227–248

    Article  Google Scholar 

  11. Dimitriadou E, Barth M, Windischberger C, Hornik K, Moser E (2004) A quantitative comparison of functional MRI cluster analysis. Artif Intell Med 31(1):57–71

    Article  Google Scholar 

  12. Ding C, He X (2003) Cluster merging and splitting in hierarchical clustering algorithms. In: IEEE international conference on data mining ICDM, pp 139–146

  13. Farid AP, Popescu AC (2004) Exposing digital forgeries by detecting duplicated image region. [Technical Report]. Hanover, Department of Computer Science, Dartmouth College

  14. Fridrich AJ, Soukal BD, Lukáá AJ (2003) Detection of copy–move forgery in digital images. In: Proceedings of digital forensic research workshop

  15. Harris C, Mike S (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15, pp 10–5244

  16. Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: Computational intelligence and industrial application, vol 2, pp 272–276

  17. Huang Y, Lu W, Sun W, Long D (2011) Improved DCT–based detection of copy–move forgery in images. Forensic Sci Int 206(1):178–184

    Article  Google Scholar 

  18. Kang X, Wei S (2009) Identifying tampered regions using singular value decomposition in digital image forensics. In: Proceeding of international conference on computer science and software engineering, vol 3, pp 926–930

  19. Kokare M, Biswas PK, Chatterji BN (2005) Texture image retrieval using new rotated complex wavelet filters. IEEE Trans Syst Man Cybern B (Cybern) 35 (6):1168–1178

    Article  Google Scholar 

  20. Lakemond R, Sridharan S, Fookes C (2012) Hessian-based affine adaptation of salient local image features. J Math Imaging Vis 44(2):150–167

    Article  MathSciNet  MATH  Google Scholar 

  21. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1):59–67

    Article  Google Scholar 

  22. Li Z, Yang Y, Zhou X, Lu H (2012) Unsupervised feature selection using nonnegative spectral analysis. In: Proceedings of national conference on association for the advancement of artificial intelligence (AAAI), vol 2, pp 1026–1032

  23. 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

    Article  Google Scholar 

  24. Li J, Yang F, Lu W, Sun W (2017) Keypoint-based copy-move detection scheme by adopting MSCRs and improved feature matching. Multimed Tools Appl 76 (20):20483–20497

    Article  Google Scholar 

  25. Li Z, Tang J, He X (2018) Robust structured nonnegative matrix factorization for image representation. IEEE Trans Neural Netw Learn Syst 29(5):1947–1960

    Article  MathSciNet  Google Scholar 

  26. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  27. Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 172(2):180–189

    Article  Google Scholar 

  28. Manu V T, Babu M (2016) Detection of copy-move forgery in images using segmentation and SURF. In: Advances in signal processing and intelligent recognition systems, pp 645–654

  29. Matas J, Chum O, Urban M, Pajdla T (2004) Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput 22(10):761–767

    Article  Google Scholar 

  30. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Van Gool L (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72

    Article  Google Scholar 

  31. Moravec HP (1977) Towards automatic visual obstacle avoidance. In: 5th international joint conference on artificial intelligence, pp 584–594

  32. Muhammad G, Hussain M, Bebisi G (2012) Passive copy–move image forgery detection using undecimated Dyadic wavelet transform. In: Digital investigation

  33. [online]: Available: http://www.dicgim.unipa.it/cvip/

  34. [online]: Image source: http://r0k.us/graphics/kodak/

  35. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–67

    Article  Google Scholar 

  36. Punj G, Stewart DW (1983) Cluster analysis in marketing research: review and suggestions for application. J Mark Res 20(2):134–148

    Article  Google Scholar 

  37. Rosten E, Drummond T (2005) Fusing points and lines for high performance tracking. In: Tenth IEEE international conference on computer vision, vol 2, pp 1508–1515

  38. Ryu SJ, Lee MJ, Lee HK (2010) Detection of copy-rotate-move forgery using zernike moments. In: International workshop on information hiding, vol 6. Springer, Berlin, pp 351–364

  39. Sergio B, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: Acoustics, speech and signal processing (ICASSP), pp 1880–1883

  40. Shivakumar BL, Baboo S (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8(4):199–205

    Google Scholar 

  41. 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):351–364

    Google Scholar 

  42. Tralic D, Zupancic I, Grgic S, Grgic M (2013) CoMoFoD–new database for copy-move forgery detection. In: 55th international symposium, pp 49–54

  43. Van De Sande K, Gevers T, Snoek C (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596

    Article  Google Scholar 

  44. Wang J, Liu G, Li H, Dai Y, Wang Z (2009) Detection of image region duplication forgery using model with circle block. In: Multimedia information networking and security, vol 1, pp 25–29

  45. Wu Z, Ke Q, Isard M, Sun J (2009) Bundling features for large scale partial-duplicate web image search. Proc IEEE Conf Comput Vis Pattern Recognit 22 (10):25–32

    Google Scholar 

  46. Wu Q, Wang S, Zhang X (2011) Log-polar based scheme for revealing duplicated regions in digital images. IEEE Signal Process Lett 18(10):559–562

    Article  Google Scholar 

  47. Xunyu P, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867

    Article  Google Scholar 

  48. Yang J, Ran P, Tan J (2013) Digital image forgery forensics by using undecimated Dyadic wavelet transform and Zernike moments. J Comput Inf Syst 9 (16):6399–6408

    Google Scholar 

  49. Yang B, Sun X, Guo H, Xia Z, Chen X (2017) A copy-move forgery detection method based on CMFD–SIFT. Multimed Tools Appl 77(1):837–855

    Article  Google Scholar 

  50. Zhang J, Feng Z, Su Y (2008) A new approach for detecting copy–move forgery in digital images. In: 11th IEEE Singapore international conference on communication systems, pp 362–366

  51. Zheng J, Chang L (2014) Detection technology of tampering image based on Harris corner points. J Comput Inf Syst 10:1481–88

    Google Scholar 

  52. Zhu Y, Shen X, Chen H (2016) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75(6):3221–3233

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Manipal University Jaipur, Jaipur, India

    Rahul Dixit

  2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, 769008, India

    Ruchira Naskar

Authors
  1. Rahul Dixit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruchira Naskar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rahul Dixit.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dixit, R., Naskar, R. Region duplication detection in digital images based on Centroid Linkage Clustering of key–points and graph similarity matching. Multimed Tools Appl 78, 13819–13840 (2019). https://doi.org/10.1007/s11042-018-6666-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6666-1

Keywords

Search

Navigation