Skip to main content
Log in

Perceptual 3D model hashing using key-dependent shape feature

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

Abstract

With the rapid growth of three-dimensional (3D) content, perceptual 3D model hashing will become a solution for the authentication, reliability, and copy detection of 3D content and will continue to be an important aspect of multimedia security in the future. However, perceptual 3D model hashing has not been used as widely as perceptual image or video hashing. In this study, a robust and secure perceptual 3D model hashing function is developed based on a key-dependent shape feature. The main objectives of our hashing function are to exhibit robustness against content-preserved attacks and to enable blind-detection without the use of preprocessing techniques for these types of attacks. In order to achieve these objectives, our hashing projects all of the vertices to the shape coordinates of the shape spectrum descriptor and the curvedness, and then, it segments the shape coordinates into irregular cells and computes the shape features of the cells using a permutation key and a random key. A perceptual hash is generated by binarizing the shape features. Experimental results confirm that the proposed hashing scheme shows robustness against geometrical and topological attacks and provides a unique and secure hash for each model and key.

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

Access this article

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

Instant access to the full article PDF.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Addabbo T, Alioto M, Fort A, Pasini A, Rocchi S, Vignoli V (2007) A class of maximum-period nonlinear congruential generators derived from the Rényi chaotic map. IEEE Trans Circuits Syst I: Regular Papers 54(4):816–828

    Article  MathSciNet  Google Scholar 

  2. Benedens O (1999) Geometry-based watermarking of 3D models. IEEE Comput Graph 19(1):46–55

    Article  Google Scholar 

  3. Bober M (2001) MPEG-7 visual shape descriptors. IEEE Trans Circuits Syst 11(4):716–719

    Google Scholar 

  4. Bustos B, Keim DA, Saupe D, Schreck T (2007) Content-based 3D object retrieval. IEEE Comput Graph 27(4):22–27

    Article  Google Scholar 

  5. Bustos B, Keim DA, Saupe D, Schreck T, Vranic DV (2005) Feature-based similarity search in 3D object databases. ACM Comput Surv (CSUR) 37(4):345–387

    Article  Google Scholar 

  6. Choi YS, Park JH (2012) Image hash generation method using hierarchical histogram. Multimed Tools Appl 61(1):181–194

    Article  Google Scholar 

  7. Coskun B, Sankur B, Memon N (2006) Spatio-temporal transform based video hashing. IEEE Trans Multimedia 8(6):1190–1208

    Article  Google Scholar 

  8. Delp EJ (2005) Multimedia security: the 22nd century approach. Multimedia Syst. 11(2):95–97

    Article  Google Scholar 

  9. Durstenfeld R (1964) Algorithm 235: Random permutation. Commun ACM 7(7):420

    Article  Google Scholar 

  10. Dyn N, Hormann K, Kim SJ, Levin D (2000) Optimizing 3D triangulations using discrete curvature analysis. Math. Methods for Curves and Surfaces, pp 135–146

  11. Eskizara O, Akagunduz E, Ulusoy I (2009) 3D object recognition by geometric hashing. In: IEEE 17th, Signal processing and communications applications conference, pp 932–935

  12. Fernandes E, Delaigle JF (2004) Geometric soft hash functions for 2D and 3D objects. In: Proc. SPIE 5306, Security, steganography, and watermarking of multimedia contents VI, pp 784–795

  13. Ghaderpanah M, Abbas A, Hamza AB (2008) Entropic hashing of 3D objects using Laplace-Beltrami operator. In: 15th IEEE International conference on image processing, pp 3104–3107

  14. Grana C, Davolio M, Cucchiara R (2007) Similarity-based retrieval with MPEG-7 3D descriptors: performance evaluation on the princeton shape benchmark. Lect Notes Comput Sci 4877:308–317

    Article  Google Scholar 

  15. Gu X, Zhang Y, Zhang L, Zhang D, Li J (2013) An improved method of locality sensitive hashing for indexing large-scale and high-dimensional features. Signal Proces 93(8):2244–2255

    Article  Google Scholar 

  16. Jagannathan A, Miller EL (2007) Three-dimensional surface mesh segmentation using curvedness-based region growing approach. IEEE Trans Pattern Anal Mach Intell 29(12):2195–2204

    Article  Google Scholar 

  17. Kanai S, Date H, Kishinami T (1998) Digital watermarking for 3D polygons using multiresolution wavelet decomposition. In: Proc. of Sixth IFIP WG 5.2 GEO-6, pp 296–307

  18. Lee SH, Kwon KR (2007) A watermarking for 3D-mesh using the patch CEGIs. Digit Signal Process 17(2):396–413

    Article  MathSciNet  Google Scholar 

  19. Lee SH, Kwon KR (2008) Mesh watermarking based projection onto two convex sets. Multimedia Syst 13(5–6):1432–1882

    Google Scholar 

  20. Lee SH, Kwon KR (2011) VRML animated model watermarking scheme using geometry and interpolator nodes. Comput Aided Design 43(8):1056–1073

    Article  Google Scholar 

  21. Lee SH, Kwon KR (2012) Robust 3D mesh model hashing based on feature object. Digit Signal Process 22(5):744–759

    Article  MathSciNet  Google Scholar 

  22. Lee SH, Kwon KR, Hwang WJ, Chandrasekar V (2013) Key-dependent 3D model hashing for authentication using heat kernel signature. Digit Signal Process. doi:10.1016/j.dsp.2013.04.012

    MathSciNet  Google Scholar 

  23. Liu F, Cheng LM, Leung HY, Fu QK (2012) Wave atom transform generated strong image hashing scheme. Opt Commun 285:5008–5018

    Article  Google Scholar 

  24. Matei B, Shan Y, Sawhney HS, Tan Y, Kumar R, Huber D, Hebert M (2006) Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation. IEEE Trans Pattern Anal Mach Intell 28(7):1111–1126

    Article  Google Scholar 

  25. Monga V, Banerjee A, Evans BL (2006) A clustering based approach to perceptual image hashing. IEEE Trans Information Forensics and Security 1(1):68–79

    Article  Google Scholar 

  26. Monga V, Mhcak MK (2007) Robust and secure image hashing via non-negative matrix factorizations. IEEE Trans Information Forensics and Security 2(3):376–390

    Article  Google Scholar 

  27. National Institute for Standards and Technology (2010) A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications, SP pp 800–22 Rev 1a

  28. Ohbuchi R, Takahashi S, Miyazawa T, Mukaiyama A (2001) Watermarking 3D polygonal meshes in the mesh spectral domain. In: Proc. of graphics interface, pp 9–17

  29. Papoulis A, Pillai SU (2002) Probability, random variables and stochastic processes. McGraw-Hill, New York

    Google Scholar 

  30. Praun E, Hoppe H, Finkelstein A (1999) Robust mesh watermarking. In: Proc. of ACM SIGGRAPH, pp 49–56

  31. Roover C, Vleeschouwer C, Lefebvre F, Macq B (2005) Robust video hashing based on radial projections of key frames. IEEE Trans Signal Process 53(10):4020–4037

    Article  MathSciNet  Google Scholar 

  32. Sehgal A, Desai UB (2003) 3D object recognition using Bayesian geometric hashing and pose clustering. Pattern Recogn 36(3):765–780

    Article  MATH  Google Scholar 

  33. Sun R, Zeng W (2012) Secure and robust image hashing via compressive sensing. Multimed Tools Appl. doi:10.1007/s11042-012-1188-8

    Google Scholar 

  34. Swaminathan A, Mao Y, Wu, M (2006) Robust and secure image hashing. IEEE Trans Information Forensics and Security 1(2):215–230

    Article  Google Scholar 

  35. Tarmissia K, Hamza AB (2009) Information-theoretic hashing of 3D objects using spectral graph theory. Expert Syst Appl 36(5):9409–9414

    Article  Google Scholar 

  36. Wu Y, Agaian S, Noonan JP (2012) A new randomness evaluation method with applications to image shuffling and encryption. arXiv:1211.1654v1[cs.CR]

  37. Zaharia T, Preteux F (2001) 3D-shape-based retrieval within the MPEG-7 framework. In: Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, pp 133–145

  38. Zhe C, Rongchun Z, Yanning Z (2006) Geometric Hashing Using 3D Aspects and Constrained Structures. In: 8th International conference on signal processing, vol 2

Download references

Acknowledgements

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MEST) (KRF-2009-0071269 and KRF-2011-0023118) and by Brain Busan (BB21) project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Suk-Hwan Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, SH., Hwang, WJ. & Kwon, KR. Perceptual 3D model hashing using key-dependent shape feature. Multimed Tools Appl 73, 1723–1755 (2014). https://doi.org/10.1007/s11042-013-1643-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1643-1

Keywords

Navigation