Skip to main content
SpringerLink
Log in

Canopy approach of image clustering based on camera fingerprints

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

Abstract

The paper presents a canopy based image clustering algorithm using normalized cross-correlation among the camera fingerprints as a decision criterion. The proposed framework uses two levels of the threshold at two different stages to cluster images based on camera fingerprints. Initially, fingerprints are sorted in descending order of their goodness, and then raw clusters are constructed using a relaxed threshold followed by fine clustering with a hard threshold. The raw and fine clustering process results in non-overlapping clusters, which avoids assigning a fingerprint to multiple raw or fine clusters. The fine clusters are further processed in the attraction phase to improve the cluster’s quality at the cost of some computation. The CIC algorithm results in high-quality clusters with a reduced computational cost. The results show that the computational complexity per fingerprint, with respect to the reference complexity of n(n − 1)/2, decreases as the size of the dataset increases. The proposed algorithm also does not suffer from the problem when the number of cameras is larger than the average number of images taken with a camera, i.e., NC ≫ SC. Hence, the algorithm is suitable for large scale clustering and solving different scenarios of NC ≫ SC.

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

Similar content being viewed by others

References

  1. Achlioptas D (2003) Database-friendly random projections: Johnson Lindenstrauss with binary coins. J Comput Syst Sci 66(4):671–687

    Article  MathSciNet  Google Scholar 

  2. Amelio A, Pizzuti C (2015) Is normalized mutual information a fair measure for comparing community detection methods?. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. ACM, Paris, pp 1584–1585

  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 For Secur 6(3):1099–1110. IEEE

    Article  Google Scholar 

  4. Bloy G J (2008) Blind camera fingerprinting and image clustering. IEEE Trans Pattern Anal Mach Intell 30(3):532–534

    Article  Google Scholar 

  5. Caldelli R, Amerini I, Picchioni F, Innocenti M (2010) Fast image clustering of unknown source images. In: Proceedings of IEEE international workshop on information forensics and security. IEEE, pp 1–5

  6. Chen M, Fridrich J, Goljan M (2007) Digital imaging sensor identification (further study). In: Security, steganography, and watermarking of multimedia contents IX, 6505,65050P. International Society for Optics and Photonics

  7. Chen M, Fridrich J, Goljan M, Lukás J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf For Secur 3(1):74–90

    Article  Google Scholar 

  8. de Souto M C P, Coelho A L V, Faceli K, Sakata T C, Bonadia V, Costa I G (2012) A comparison of external clustering evaluation indices in the context of imbalanced data sets. In: 2012 Brazilian symposium on neural networks. Bellingham, pp 49–54

  9. Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2790–2797

  10. Equitz W H (1989) A new vector quantization clustering algorithm. IEEE Trans Acoust Speech Signal Process 37(10):1568–1575

    Article  Google Scholar 

  11. Ester M, Kriegel H P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96 (34):226–231

    Google Scholar 

  12. Fahmy OM (2015) An efficient clustering technique for cameras identification using sensor pattern noise. In: Proceedings of international conference on systems, signals and image process. IEEE, pp 249–252

  13. Filler T, Fridrich J, Goljan M (2008) Using sensor pattern noise for camera model identification. In: 15th IEEE international conference on image processing. IEEE, pp 1296–1299

  14. Georgievska S, Bakhshi R, Gavai A, Sclocco A, van Werkhoven B (2017) Clustering image noise patterns by embedding and visualization for common source camera detection. Digit Investig 23:22–30

    Article  Google Scholar 

  15. Gisolf F, Barens P, Snel E, Malgoezar A, Vos M, Mieremet A, Geradts Z (2014) Common source identification of images in large databases. Forensic Sci Int 44:222–230

    Article  Google Scholar 

  16. Gloe T, Böhme R (2010) The ‘Dresden image database’ for benchmarking digital image forensics. In: Proceedings of the 2010 ACM symposium on applied computing. ACM, pp 1584–1590

  17. Gloe T, Pfennig S, Kirchner M (2012) Unexpected artefacts in PRNU based camera identification: a ‘Dresden image database’ case-study. In: Proceedings of ACM workshop multimedia security. ACM, pp 109–114

  18. Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. ACM Sigmod Record 27(2):73–84. ACM

    Article  Google Scholar 

  19. Guha S, Rastogi R, Shim K (2000) ROCK: a robust clustering algorithm for categorical attributes. Inf Syst 25(5):345–366. Elsevier

    Article  Google Scholar 

  20. Haralick R M, Shapiro L G (1992) Computer and robot vision. Addison-Wesley

  21. Holst G C (1998) CCD Arrays, cameras, and displays. Citeseer

  22. Hubert L, Arabie P (1985) Comparing partitions. J Classif 2:193–218

    Article  Google Scholar 

  23. Huffman M, Steinley D, Brusco M J (2015) A note on using the adjusted Rand index for link prediction in networks. Social Netw 42:72–79

    Article  Google Scholar 

  24. Jain A K, Dubes R C (1988) Algorithms for clustering data. Prentice-Hall, Inc

  25. Janesick J R (2001) Scientific charge-coupled devices, 117. SPIE Press, Bellingham

    Book  Google Scholar 

  26. Jin X, Han J (2011) Expectation maximization clustering. In: Encyclopedia of machine learning. Springer, pp 382–383

  27. Kanungo T, Mount D M, Netanyahu N S, Piatko C D, Silverman R, Wu A Y (2002) An efficient k-means clustering algorithm: analysis and implementation, vol 7. IEEE, pp 881–892

  28. Karypis G, Han EH, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. Computer 32(8):68–75. IEEE

    Article  Google Scholar 

  29. Kennedy D (2006) Editorial retraction. Science 211(5759):335–335

    Article  Google Scholar 

  30. Khan S, Bianchi T (2019) Reduced complexity image clustering based on camera fingerprints. In: 2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2682–2688

  31. Khan S, Bianchi T (2019) Fast image clustering based on camera fingerprint ordering. In: International conference on multimedia and expo (ICME) 2019. IEEE, Shanghai, pp 766–771

  32. Li C T (2010) Unsupervised classification of digital images using enhanced sensor pattern noise. In: Proceedings of IEEE international symposium on circuits and systems. IEEE, pp 3429–3432

  33. Li C T (2010) Source camera identification using enhanced sensor pattern noise. IEEE Trans Acoust Speech Signal Process 5(2):280–287

    Google Scholar 

  34. Li C T, Li Y (2010) Digital camera identification using colour-decoupled photo response non-uniformity noise pattern. In: Proceedings of 2010 IEEE international symposium on circuits and systems. IEEE, pp 3052–3055

  35. Li C T, Lin X (2017) A fast source-oriented image clustering method for digital forensics. EURASIP J Image Video Process 1:69

    Article  Google Scholar 

  36. Li P, Hastie T J, Church K W (2006) Very sparse random projections. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 287–296

  37. Lin X, Li C T (2017) Large-scale image clustering based on camera fingerprints. IEEE Trans Inf For Secur 12(4):793–808

    Google Scholar 

  38. Liu B B, Lee H K, Hu Y, Choi C H (2013) On classification of source cameras: a graph based approach. In: IEEE international workshop on information forensics and security. IEEE, pp 1–5

  39. Lukás J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf For Secur 1(2):205–214

    Article  Google Scholar 

  40. Marra F, Poggi G, Sansone C, Verdoliva L (2017) Blind PRNU-based image clustering for source identification. IEEE Trans Inf For Secur 12 (9):2197–2211

    Article  Google Scholar 

  41. McCallum A, Nigam K, Ungar L H (2009) Efficient clustering of high-dimensional data sets with application to reference matching. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 169–178

  42. Murtagh F, Contreras P (2012) Algorithms for hierarchical clustering: an overview. Wiley Interdiscip Rev: Data Min Knowl Discov 2(1):86–96. Wiley Online Library

    Google Scholar 

  43. Ng RT, Han J (2002) CLARANS: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5):1003–1016. IEEE

    Article  Google Scholar 

  44. Pearson H (2005) Image manipulation: CSI: cell biology. Nature 434:952–953

    Article  Google Scholar 

  45. Phan Q T, Boato G, De Natale F G (2017) Image clustering by source camera via sparse representation. In: Proceedings of the 2nd international workshop on multimedia forensics and security. ACM, pp 1–5

  46. Phan Q T, Boato G, De Natale F G (2018) Accurate and scalable image clustering based on sparse representation of camera fingerprint. arXiv:1810.07945

  47. Rand W M (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

    Article  Google Scholar 

  48. Villalba L G, Orozco A S, Corripio J R (2015) Smartphone image clustering. Expert Syst Appl 42:1927–1940

    Article  Google Scholar 

  49. Vinhn N X, Epps J, Bailey J (2009) Information theoretic measures for clusterings comparison: is a correction for chance necessary?. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 1073–1080

  50. Yeung K Y, Ruzzu W L (2001) Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9):763–774

    Article  Google Scholar 

  51. Yu S X, Shi J (2003) Multiclass spectral clustering. In: IEEE international conference on computer vision. IEEE, pp 313–319

  52. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Record 25(2):103–114. ACM

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Telecommunication Engineering, University of Engineering and Technology, Mardan, Pakistan

    Sahib Khan

Authors
  1. Sahib Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sahib Khan.

Ethics declarations

Conflict of interest

The author declare that he have no conflict of interest.

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

Khan, S. Canopy approach of image clustering based on camera fingerprints. Multimed Tools Appl 81, 21591–21618 (2022). https://doi.org/10.1007/s11042-022-12463-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12463-5

Keywords

Search

Navigation