New dissimilarity measures for image phylogeny reconstruction

Industrial and Commercial Application
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Abstract

Image phylogeny is the problem of reconstructing the structure that represents the history of generation of semantically similar images (e.g., near-duplicate images). Typical image phylogeny approaches break the problem into two steps: (1) estimating the dissimilarity between each pair of images and (2) reconstructing the phylogeny structure. Given that the dissimilarity calculation directly impacts the phylogeny reconstruction, in this paper, we propose new approaches to the standard formulation of the dissimilarity measure employed in image phylogeny, aiming at improving the reconstruction of the tree structure that represents the generational relationships between semantically similar images. These new formulations exploit a different method of color adjustment, local gradients to estimate pixel differences and mutual information as a similarity measure. The results obtained with the proposed formulation remarkably outperform the existing counterparts in the literature, allowing a much better analysis of the kinship relationships in a set of images, allowing for more accurate deployment of phylogeny solutions to tackle traitor tracing, copyright enforcement and digital forensics problems.

Keywords

Digital forensics Image phylogeny reconstruction Mutual information Dissimilarity calculation 

Supplementary material

10044_2017_616_MOESM1_ESM.pdf (92 kb)
Supplementary material 1 (pdf 92 KB)

References

  1. 1.
    Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw (TNN) 5(4):537–550CrossRefGoogle Scholar
  2. 2.
    Bay H, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Elsevier Comput Vis Image Underst 110(3):346–359CrossRefGoogle Scholar
  3. 3.
    Bestagini P, Tagliasacchi M, Tubaro S (2016) Image phylogeny tree reconstruction based on region selection. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2059–2063Google Scholar
  4. 4.
    Bramon R, Boada I, Bardera A, Rodriguez J, Feixas M, Puig J, Sbert M (2012) Multimodal data fusion based on mutual information. IEEE Trans Vis Comput Graph (TVCG) 18(9):1574–1587CrossRefGoogle Scholar
  5. 5.
    Brownlee KA (1965) Statistical theory and methodology in science and engineering. Wiley series in probability and mathematical statistics: applied probability and statistics. Wiley, New YorkMATHGoogle Scholar
  6. 6.
    Costa F, Lameri S, Bestagini P, Dias Z, Rocha A, Tagliasacchi M, Tubaro S (2015) Phylogeny reconstruction for misaligned and compressed video sequences. In: IEEE international conference on image processing (ICIP), pp 301–305Google Scholar
  7. 7.
    Costa F, Lameri S, Bestagini P, Dias Z, Tubaro S, Rocha A (2016) Hash-based frame selection for video phylogeny. In: IEEE international workshop on information forensics and security (WIFS)Google Scholar
  8. 8.
    Costa F, Oikawa M, Dias Z, Goldenstein S, Rocha A (2014) Image phylogeny forests reconstruction. IEEE Trans Inf Forensics Secur (TIFS) 9(10):1533–1546CrossRefGoogle Scholar
  9. 9.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 886–893Google Scholar
  10. 10.
    de Oliveira A, Ferrara P, De Rosa A, Piva A, Barni Mauro, Goldenstein S, Dias Z, Rocha A (2016) Multiple parenting phylogeny relationships in digital images. IEEE Trans Inf Forensics Secur(TIFS) 11(2):328–343CrossRefGoogle Scholar
  11. 11.
    Dias Z, Goldenstein S, Rocha A (2013) Exploring heuristic and optimum branching algorithms for image phylogeny. Elsevier J Vis Commun Image Represent 24:1124–1134CrossRefGoogle Scholar
  12. 12.
    Dias Z, Goldenstein S, Rocha A (2013) Large-scale image phylogeny: tracing back image ancestry relationships. IEEE Multimed 20:58–70CrossRefGoogle Scholar
  13. 13.
    Dias Z, Goldenstein S, Rocha A (2013) Toward image phylogeny forests: automatically recovering semantically similar image relationships. Elsevier Forensic Sci Int (FSI) 231:178–189CrossRefGoogle Scholar
  14. 14.
    Dias Z, Rocha A, Goldenstein S (2010) First steps toward image phylogeny. In: IEEE international workshop on information forensics and security (WIFS), pp 1–6Google Scholar
  15. 15.
    Dias Z, Rocha A, Goldenstein S (2011) Video phylogeny: recovering near-duplicate video relationships. In: IEEE international workshop on information forensics and security (WIFS), pp 1–6Google Scholar
  16. 16.
    Dias Z, Rocha A, Goldenstein S (2012) Image phylogeny by minimal spanning trees. IEEE Trans Inf Forensics Secur (TIFS) 7(2):774–788CrossRefGoogle Scholar
  17. 17.
    Edmonds J (1967) Optimum branchings. J Res Natl Inst Stand Technol 71B:48–50MathSciNetMATHGoogle Scholar
  18. 18.
    Fan Z, De Queiroz RL (2003) Identification of bitmap compression history: Jpeg detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235CrossRefGoogle Scholar
  19. 19.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. ACM Commun 24(6):381–395MathSciNetCrossRefGoogle Scholar
  20. 20.
    Gonzalez R, Woods R (2007) Digital image processing, 3rd edn. Prentice-Hall, New JerseyGoogle Scholar
  21. 21.
    Goshtasby AA (2012) Image registration: principles, tools and methods. advances in computer vision and pattern recognition, 1st edn. Springer, New YorkCrossRefMATHGoogle Scholar
  22. 22.
    Hong C, Yu J, Tao D, Wang M (2015) Image-based three-dimensional human pose recovery by multiview locality-sensitive sparse retrieval. IEEE Trans Ind Electron 62(6):3742–3751Google Scholar
  23. 23.
    Joly A, Buisson O, Frélicot C (2007) Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans Multimed 9(2):293–306CrossRefGoogle Scholar
  24. 24.
    Kender JR, Hill ML, Natsev AP, Smith JR, Xie L (2010) Video genetics: a case study from youtube. In: International conference on multimedia, pp 1253–1258Google Scholar
  25. 25.
    Kennedy L, Chang S-F (2008) Internet image archaeology: automatically tracing the manipulation history of photographs on the web. In: ACM international conference of multimedia, pp 349–358Google Scholar
  26. 26.
    Lameri S, Bestagini P, Melloni A, Milani S, Rocha A, Tagliasacchi M, Tubaro S (2014) Who is my parent? Reconstructing video sequences from partially matching shots. In: IEEE international conference on image processing (ICIP), pp 5342–5346Google Scholar
  27. 27.
    MacKinnon JG (1996) Numerical distribution functions for unit root and cointegration tests. J Appl Econom 11(6):601–618CrossRefGoogle Scholar
  28. 28.
    Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2):187–198CrossRefGoogle Scholar
  29. 29.
    Mao J, Bulan O, Sharma G, Datta S (2009) Device temporal forensics: an information theoretic approach. In: IEEE international conference on image processing, pp 1485–1488Google Scholar
  30. 30.
    Melloni A, Bestagini P, Milani S, Tagliasacchi M, Rocha A, Tubaro S (2014) Image phylogeny through dissimilarity metrics fusion. In: IEEE European workshop on visual information processing (EUVIP), pp 1–6Google Scholar
  31. 31.
    Melloni A, Lameri S, Bestagini P, Tagliasacchi M, Tubaro S (2015) Near-duplicate detection and alignment for multi-view videos. In: IEEE international conference on image processing (ICIP), pp 1–4Google Scholar
  32. 32.
    Milani S, Fontana M, Bestagini P, Tubaro S (2016) Phylogenetic analysis of near-duplicate images using processing age metrics. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2054–2058Google Scholar
  33. 33.
    Nucci M, Tagliasacchi M, Tubaro S (2013) A phylogenetic analysis of near-duplicate audio tracks. In: IEEE international workshop on multimedia, signal processing, pp 99–104Google Scholar
  34. 34.
    Oikawa M, Dias Z, Rocha A, Goldenstein S (2016) Manifold learning and spectral clustering for image phylogeny forests. IEEE Trans Inf Forensics Secur 11(1):5–18CrossRefGoogle Scholar
  35. 35.
    Oliveira A, Ferrara P, De Rosa A, Piva A, Barni M, Goldenstein S, Dias Z, Rocha A (2014) Multiple parenting identification in image phylogeny. In: IEEE international conference on image processing (ICIP), pp 5347–5351Google Scholar
  36. 36.
    Oliveira A, Ferrara P, De Rosa A, Piva A, Barni M, Goldenstein S, Dias Z, Rocha A (2016) Multiple parenting phylogeny relationships in digital images. IEEE Trans Inf Forensics and Secur (TIFS) 11(2):328–343CrossRefGoogle Scholar
  37. 37.
    Reinhard E, Ashikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graph Appl 21:34–41CrossRefGoogle Scholar
  38. 38.
    De Rosa A, Uccheddu F, Costanzo A, Piva A, Barni M (2010) Exploring image dependencies: a new challenge in image forensics. SPIE Med Forensics Secur 7541(2):1–12Google Scholar
  39. 39.
    Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB: an efficient alternative to SIFT or SURF. In: IEEE international conference on computer vision (ICCV), pp 2564–2571Google Scholar
  40. 40.
    Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(379–423):623–656MathSciNetCrossRefMATHGoogle Scholar
  41. 41.
    Sobel I, Feldman G (1968) A \(3\times 3\) isotropic gradient operator for image processing. In: Artificial Project in a talk at the Stanford, pp 271–272Google Scholar
  42. 42.
    Tapia JE, Perez CA (2013) Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. IEEE Trans Inf Forensics Secury (TIFS) 8(3):488–499CrossRefGoogle Scholar
  43. 43.
    Viola P, Wells WM (1997) Alignment by maximization of mutual information. Int J Comput Vis 24:137–154CrossRefGoogle Scholar
  44. 44.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83CrossRefGoogle Scholar
  45. 45.
    Yu J, Rui Y, Chen B (2014) Exploiting click constraints and multi-view features for image re-ranking. IEEE Trans Multimed 16(1):159–168CrossRefGoogle Scholar
  46. 46.
    Yu J, Yang X, Gao F, Tao D (2016) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern. doi:10.1109/TCYB.2016.2591583 Google Scholar
  47. 47.
    Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil
  2. 2.Università degli Studi di FirenzeFlorenceItaly

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