Skip to main content
SpringerLink
Log in

Perceptual image hashing based on structural fractal features of image coding and ring partition

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

Abstract

Perceptual image hashing finds increasing attention in several multimedia security applications. However, reaching the trade-off balance between the two most important properties of image hashing− robustness and discrimination, still remains the most restive challenge in hashing schemes. In this study, a robust image hashing technique is proposed by incorporating ring partition and fractal image coding. The scheme starts by normalizing the image to help in extracting its local features. Then the concept of ring partition is introduced in order to make our hash rotation invariant by dividing the image into 5 different rings to form a secondary image that possesses the invariant property. Further, image coding is introduced by extracting the structural fractal features to exploit dimensionality reduction and compression, hence, generating a robust hash. To ensure the system’s security, encryption is performed on the generated fractal elements before the final hash construction. We conduct series of experiments to evaluate the performance of our scheme. The achieved result shows that our scheme is robust against several content-preserving attacks such as image rotation, JPEG compression, gamma correction, gaussian low pass filtering, image scaling, cropping, brightness adjustment and contrast adjustment. In addition, the receiver operating characteristics is used to show the discriminative capability and robustness of our scheme as compared to other state-of-art schemes in the literature.

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

Similar content being viewed by others

References

  1. Abate AF , Barra S, Casanova A , Fenu G, Marras M (2018) Iris Quality Assessment: A Statistical Approach for Biometric Security Applications. In International Symposium on Cyberspace Safety and Security, Amalfi

  2. Abdullahi SM, Wang H (2017) Robust enhancement and centroid-based concealment of fingerprint biometric data into audio signals. Mult Tools Appls 77(16):20753–20782

    Article  Google Scholar 

  3. Abdullahi SM, Wang H (2018) Fourier-Mellin transform and fractal coding for secure and robust fingerprint image hashing. 15th IEEE International Conference on Advanced Video and Signal Based Surveillance AVSS, Auckland

  4. Abdullahi SM, Wang H, Qian Q, Cao W (2017) Concealing Fingerprint-Biometric Data into Audio Signals for Identify Authentication. Digital Forensics and Watermarking IWDW, Beijing

    Book  Google Scholar 

  5. Abdullahi SM, Wang H, Malik A (2018) Fingerprint image hashing based on minutiae points and shape context. Int J Digital Crim Foren (IJDCF) 10(4):1–20

    Article  Google Scholar 

  6. Ahmed F, Siyal M, Abbas V (2010) A secure and robust hash-based scheme for image authentication. Signal Process 90(5):1456–1470

    Article  MATH  Google Scholar 

  7. Choi Y, Park J (2012) Image hash generation method using hierarchical histogram. Multimed Tool Appl 61(1):181–194

    Article  Google Scholar 

  8. Ghouti L (2014) Robust perceptual color image hashing using quaternion singular value decomposition. Proceedings of IEEE international conference on acoustic, speech and signal processing

  9. Govindaraj P, Sandeep R (2015) Ring partition and DWT based perceptual image hashing with application to indexing and retrieval of near-identical images. International conference on advances in computing and comm

  10. Guo X, Hatzinakos D (2007) Content based image hashing via wavelet and radon transform. The 8th pacific rim conference on multimedia, Hongkong, China

  11. Khelifi F, Jiang J (2010) Analysis of the security of perceptual image hashing based on non-negative matrix factorization. IEEE Signal Process Lett 17(1):43–46

    Article  Google Scholar 

  12. Kozat S, Venkatesan R, Mihcak M (2004) Robust perceptual image hashing via matrix invariants. Proceedings of IEEE international conference on image processing

  13. Lefebvre F, Macq B, Legat J-D (2002) RASH: Radon soft hash algorithm. Proc. of European signal processing conference, Toulouse, France

  14. Lei Y, Wang Y, Huang J (2011) Robust image hash in radon transform domain for authentication. Signal Process Image Commun 26(6):280–288

    Article  Google Scholar 

  15. Monga V, Evans B (2006) Perceptual image hashing via feature points: performance evaluation and trade-offs. IEEE Image Proces 15(11):3453–3466

    Google Scholar 

  16. Monga V, Mihcak M (2007) Robust and secure image hashing via non-negative matrix factorization. IEEE Trans Info Foren Sec 2(3):376–390

    Article  Google Scholar 

  17. Open Source Database (2018). [Online]. Available: http://imageprocessingplace.com/root_files_V3/image_databases.htm. (Accessed 5 Feb 2018).

  18. Ou Y, Rhee K (2009) A key-dependent secure image hashing scheme by using radon transform. Proc. of the IEEE international symposium on intelligent signal processing and communication systems

  19. Qin C, Chang C-C, Tsou P-L (2013) Robust image hashing using non-uniform sampling in discrete fourier traansform. Digital Signal Process 23(2):578–585

    Article  MathSciNet  Google Scholar 

  20. Qin C, Chen X, Ye D, Wang J, Sun X (2016) A novel image hashing scheme with perceptual robustness using block truncation coding. Inf Sci 361(362):84–99

    Article  Google Scholar 

  21. Qin C, Jin P, Zhang X, Dong J, Wang J (2017) Fragile image watermarking with pixel-wise recovery based on overlapping embedding strategy. Signal Process 138:280–293

    Article  Google Scholar 

  22. Qin C, Sun M, Chang C-C (2018) Perceptual hashing for color images based on hybrid extraction of structural features. Signal Process 142:194–205

    Article  Google Scholar 

  23. Qin C, Chen X, Luo X, Zhang X, Sun X (2018) Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inf Sci 423:284–302

    Article  MathSciNet  Google Scholar 

  24. Schneider M, Chang S (1996) A robust content based digital signature for image authentication. Proceedings of IEEE international conference on image processing, Switzerland

  25. Sun R, Yan X, Ding Z (2011) Robust image hashing using locally linear embedding. International conference on computer science and service systems(CSSS)

  26. Swaminathan A, Mao Y, Wu M (2006) Robust and secure image hashing. IEEE Trans Info Foren Sec 1(2):215–230

    Article  Google Scholar 

  27. Tang Z, Wang S, Zhang X, Wei W, Su S (2008) Robust image hashing for tamper detection using non-negative matrix factorization. J Ubiquitous Converg Technol 52(2–3):18–26

    Google Scholar 

  28. Tang Z, Wang S, Zhang X, Wei W, Zhao Y (2011) Lexicographical framework for image hashing with implementation based on DCT and NMF. Multimed Tools Appl 52(2–3):325–345

    Article  Google Scholar 

  29. Tang Z, Dai Y, Zhang X, Zhang S (2012a) Perceptual image hashing with histogram of color vector angles. Lecture notes on Comput Sci

  30. Tang Z, Huang L, Dai Y, Yang F (2012b) Robust image hashing based on multiple histograms. Int J Digital Cont Technol Appl 6(23):39–47

    Google Scholar 

  31. Tang Z, Zhang X, Zhang S (2014) Robust perceptual image hashing based on ring partition and NMF. IEEE Trans Knowledge Data Eng 26(3):711–724

    Article  Google Scholar 

  32. Tang Z, Yang F, Huang L, Zhang X (2014b) Robust image hashing with dominant DCT coefficients. Optik 125(18):5102–5107

    Article  Google Scholar 

  33. Tang Z, Zhang X, Li X, Zhang S (2016) Robust image hashing with ring partition and invariant vector distance. IEEE Trans Info Foren Sec 11(1):200–214

    Article  Google Scholar 

  34. Tang Z, Lao H, Zhang X, Liu K (2016) Robust image hashing via DCT and LLE. J Comput Sec 62:133–148

    Article  Google Scholar 

  35. Tang Z, Huang Z, Zhang X, Lao H (2017) Robust image hashing with multi-dimensional scaling. Signal Process 137:240–250

    Article  Google Scholar 

  36. Tang Z, Chen L, Zhang X, Zhang S (2018) Robust image hashing with tensor decomposition. IEEE Trans on knowledge and data engineering, no. 99, p. early access

  37. USC-SIPI (2018) Image Database, , [Online]. Available: http://sipi.usc.edu/database/database.php. (Accessed 10 March 2018)

  38. Venkatesan R, Koon S-M, Jakubowski M, Moulin P (2000) Robust image hashing. Proceedings of the IEEE international conference on image processing, Vancouver, Canada

  39. Wu D, Zhou X, Niu X (2009) A novel image hash algorithm resistant to print–scan. Signal Process 89(12):2415–2424

    Article  MATH  Google Scholar 

  40. Xiang S, Kim H-J, Huang J (2007) Histogram-based image hashing scheme robust against geometric deformations. ACM Multimedia and Security Workshop, Dallas

  41. Zhang X, Xiao Y, Zhao Z (2015) Self-embedding fragile watermarking based on DCT and fast fractal coding. Multimed Tools Appl 74:5767–5786

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Sani M. Abdullahi for his helpful guidance throughout the review process of this manuscript.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jioatong University, Chengdu, 610031, People’s Republic of China

    Fares Khelaifi & HongJie He

Authors
  1. Fares Khelaifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. HongJie He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fares Khelaifi.

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

Khelaifi, F., He, H. Perceptual image hashing based on structural fractal features of image coding and ring partition. Multimed Tools Appl 79, 19025–19044 (2020). https://doi.org/10.1007/s11042-020-08619-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08619-w

Keywords

Search

Navigation