Multimedia Tools and Applications

, Volume 70, Issue 3, pp 1651–1665 | Cite as

Secure and robust image hashing via compressive sensing



Image hash functions find extensive applications in content authentication, database search. This paper develops a novel algorithm for generating a secure and robust image hash based on compressive sensing and Fourier-Mellin transform. Firstly, we incorporate Fourier-Mellin transform into our method to improve its performance under rotation, scale, transition attacks. Secondly, we exploit the property of dimension reduction inherent in compressive sensing for hash design. The statistic structure and sparse of the wavelet coefficients assure efficient compression in situation of including maximum the image features. The hashing method is computationally secure without additional randomization process. Such a combined approach is capable of tackling all types of attacks and thus can yield a better overall performance in multimedia identification. To demonstrate the superior performance of the proposed schemes, receiver operating characteristics analysis over a large image database is performed. Experimental results show that the proposed image hashing is robust to a wide range of distortions and attacks. When compared with the current state-of-the-art methods, the proposed method yields better identification performances under geometric attacks such as rotation attacks and brightness changes.


Compressive sensing Fourier-Mellin transform Image hashing Image identification 



This work was supported by the Natural Science Foundation of China (61001201). The authors appreciate the anonymous reviewers for their constructive comments.


  1. 1.
    Ababneh S, Ansari R, Khokhar A (2008) Scalable multimedia-content integrity verification with robust hashing. in Proceedings of IEEE International Conference on Electro/Information Technology, 263–266Google Scholar
  2. 2.
    Blumensath T, Davies EM (2009) Sampling theorems for signals from the union of linear subspaces. IEEE Trans Inf Theory 55(4)Google Scholar
  3. 3.
    Candès E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52:489–509CrossRefMATHGoogle Scholar
  4. 4.
    Candés E, Wakin BM (2008) An introduction to compressive sampling: a sensing/sampling paradigm that goes against the common knowledge in data acquisition. IEEE Signal Process Mag 25(2):21–30CrossRefGoogle Scholar
  5. 5.
    Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52:1289–1306CrossRefMathSciNetGoogle Scholar
  6. 6.
    Fridrich J, Goljan M (2000) Robust hash functions for digital watermarking. in Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC ’00),178–183Google Scholar
  7. 7.
    Gerold L, Andreas U (2008) Key-dependent JPEG2000-based robust hashing for secure image authentication. EURASIP J Inf Secur 8(1):1–19Google Scholar
  8. 8.
    He L, Carin L (2009) Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans Signal Process 57(9):3488–3497CrossRefMathSciNetGoogle Scholar
  9. 9.
    Kailasanathan C, Naini SR (2001) Image authentication surviving acceptable modifications using statistical measures and k-mean segmentation. Proc. IEEE-EURASIP Work. Nonlinear Sig. ImageGoogle Scholar
  10. 10.
    Kailasanathan C, Naini S R, Ogunbona P (2003) Compression tolerant DCT based image hash. in: Proceedings of International Conference on Distributed Computing Systems, 562–567Google Scholar
  11. 11.
    Kang WL, Lu SC, Hsu YC (2009) Compressive sensing-based image hashing, in Proc. of 2009 IEEE Int. Conf on Image Processing, Cairo, Egypt, November 2009: 1285–1288Google Scholar
  12. 12.
    Kozat S, Venkatesan R, Mihcak KM (2004) Robust perceptual image hashing via matrix invariants. Proc IEEE Intl Conf Image Process 5:3443–3446Google Scholar
  13. 13.
    Lee D, Seung H (2001) Algorithms for non-negative matrix factorization. Adv Neural Inform Process Syst 13:556–562Google Scholar
  14. 14.
    Lew M, Sebe N, Djeraba C, Jain R (2006) Content based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19CrossRefGoogle Scholar
  15. 15.
    Lin S, Ozsu TM, Oria V, and Ng R (2001) An extendible hash for multi-precision similarity querying of image databases. in Proc. 27th Very Large Data Bases (VLDB) ConferenceGoogle Scholar
  16. 16.
    Lv XD, Wang Jane Z (2008) Fast Johnson-Lindenstrauss Transform for Robust and Secure Image Hashing. Proc. of IEEE MMSP, 725–729Google Scholar
  17. 17.
    Lv XD, Wang Jane Z (2009) An Extended Image Hashing Concept: Content-Based Fingerprinting Using FJLT. EURASIP Journal on Information SecurityGoogle Scholar
  18. 18.
    Menezes A, Oorschot V, Vanstone S (1998) Handbook of applied cryptography Boca Raton. CRC, FLGoogle Scholar
  19. 19.
    Mihcak KM, Venkatesan R (2001) Video watermarking using image hashing. Microsoft Research Tech. ReportGoogle Scholar
  20. 20.
    Monga V (2005) Perceptually based methods for robust image hashing, Dissertation, University of TexasGoogle Scholar
  21. 21.
    Monga V, Evans LB (2006) Perceptual image hashing via feature points: performance evaluation and tradeoffs. IEEE Trans Image Process 15(11):3452–3465CrossRefGoogle Scholar
  22. 22.
    Monga V, Mihcak KM (2007) Robust and secure image hashing via non-negative matrix factorizations. IEEE Trans Inform Forensic Secur 2(3):376–390CrossRefGoogle Scholar
  23. 23.
    Object and Concept Recognition for Content-Based Image Retrieval, University of Washington.
  24. 24.
    Seo SJ, Haitsma J, Kalker T, Yoo DC (2004) A robust image fingerprinting system using the radon transform. Signal Process Image Comm 19(4):325–339CrossRefGoogle Scholar
  25. 25.
    Swaminathan A, Mao Y, Wu M (2006) Robust and secure image hashing. IEEE Trans Inform Forensic Secur 1(2):215–230CrossRefGoogle Scholar
  26. 26.
    Tagliasacchi M, Valenzise G, Tubaro S (2009) Hash-based identification of sparse image tampering. IEEE Trans Image Process 18(11):2491–2504CrossRefMathSciNetGoogle Scholar
  27. 27.
    Venkatesan R, Koon MS, Jakubowski HM, Moulin P (2000) Robust image hashing. in Proceedings of the International Conference on Image Processing (ICIP ’00), vol. 3: 664–666Google Scholar
  28. 28.
    Wood J (1996) Invariant pattern recognition: a review. Pattern Recogn 29(1):1–17CrossRefGoogle Scholar
  29. 29.
    Wu WC (2002) On the design of content-based multimedia authentication systems. IEEE Trans Multimed 4(3):385–393CrossRefGoogle Scholar
  30. 30.
    Wu D, Zhou XB, Niu XM (2009) A novel image hash algorithm resistant to print-scan. Signal process 89:2415–2424CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of Computer and InformationHefei University of TechnologyHefeiPeople’s Republic of China
  2. 2.Dept. of Computer ScienceUniversity of Missouri-ColumbiaColumbiaUSA

Personalised recommendations