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Robust image authentication via locality sensitive hashing with core alignment

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Abstract

Robust image hashing is a promising technique to represent image’s perceptual content. However, when it comes to image authentication, tradeoff between robustness and discrimination is a non-negligible issue. The allowed content preserving operations and sensitive malicious manipulations on images are quite subjective to human’s perception. So it needs tactics to design good hashing methods. In this paper we incorporate the novel concept of core alignment into hashing, where the proposed core alignment improves the performances of balance. First, we formulize the hashing as a supervised minimal optimization problem based on Locality Sensitive Hashing, in which p-stable distribution is exploited to maintain high dimensional locality features. Then we solve this problem by two sub-optimization problems, i.e., searching for optimal shift and searching for optimal quantization intervals. By using particle swarm optimization and simulated annealing programming approaches we develop two stochastic solutions to those two problems, respectively. Experimental results show that our proposed hashing optimizations can find optimal solutions with limited steps, and the hashing method is superior to other state-of-the-art methods in terms of authentication and robustness.

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References

  1. Bai X, Yang H, Zhou J, Ren P, Cheng J (2014) Data-dependent hashing based on p-stable distribution. IEEE Trans Image Process 23(2):5033–5046

    Article  MathSciNet  MATH  Google Scholar 

  2. Brenden C, Vinod C (2010) Robust image hashing using higher order spectral features. In: digital image computing: techqiues and applications, pp 100-104

  3. Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. SGC. Brooklyn, New York, In, pp 253–262

    MATH  Google Scholar 

  4. Galigekere RR, Holdsworth DW, Swamy MNS, Fenster A (2000) Moment patterns in the Radon space, opt. Eng 39(4):1088–1097

    Google Scholar 

  5. Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on very large data bases, San Francisco, pp 518-529

  6. Indyk P (2000) Stable distributions, pseudorandom generators, embeddings and data stream computation. In: foundations of computer Science, 2000. Proceedings. 41st annual symposium on. Redondo Beach, pp 189-197

  7. Kailasanathan C, Naini RS, Ogunbona P (2001) Image authentication surviving acceptable modifications using statistical measures and K-mean segmentation. In: Proceedings of IEEE-EURASIP work: nonlinear signal and image, pp 1-13

  8. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE International conference on Neural Networks, Perth, pp 1942–1948

  9. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by Simulated Annealing Science 220:671–680

  10. 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 

  11. Li Y (2013) Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci Int 224(1–3):59–67

    Article  Google Scholar 

  12. Li Y, Lu Z, Zhu C, Niu X (1963-1980) Robust image hashing based on random Gabor filtering and dithered lattice vector quantization. IEEE Trans Image Process 21(4):2012

    MathSciNet  MATH  Google Scholar 

  13. Li C, Yang S, Nguyen TT (2012) A Self-Learning Particle Swarm Optimizer for Global Optimization Problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42(3):627–646

    Article  Google Scholar 

  14. Mao YN, Wu M (2007) Unicity distance of robust image hashing. IEEE transactions on information forensics and security 2(3):462–467

    Article  MathSciNet  Google Scholar 

  15. Monga V, Evans BL (2006) Perceptual image hashing via feature points: performance evaluation and tradeoffs. IEEE Trans Image Process 15(11):3453–3466

    Article  Google Scholar 

  16. Monga V, Mihcak MK (2007) Robust and secure image hashing via non-negtive factorizations. IEEE transactions on information forensics and security 2(3):376–390

    Article  Google Scholar 

  17. Monga V, Banerjee A, Evans BL (2006) A clustering based approach to perceptual image hashing. IEEE transactions on information forensics and security 1(1):68–79

    Article  Google Scholar 

  18. Nan LY, Ping W, Ting SY (2015) Robust image hashing based on selective quaternion invariance. IEEE signal processing letters 22(12):2396–2400

    Article  Google Scholar 

  19. Olmos A, Kingdom FAA McGill Calibrated Colour Image Database http://tabby.vision.mcgill.ca

  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. Slaney M, Casey M (2008) Locality-sensitive hashing for finding nearest neighbors. IEEE Signal Process Mag 25(2):128–131

    Article  Google Scholar 

  22. Smith KI, Everson RM, Fieldsend JE, Murphy C, Misra R (2008) Dominance-based Multiobjective simulated annealing. IEEE Trans Evol Comput 12(3):323–342

    Article  Google Scholar 

  23. Sun R, Zeng W (2014) Secure and robust image hashing via compressive sensing. Multimed Tools Application 2014(70):1651–1665

    Article  Google Scholar 

  24. Swaminathan A, Mao YN, Wu M (2006) Robust and secure image hashing. IEEE transactions on information forensics and security 1(2):215–230

    Article  Google Scholar 

  25. Tang Z, Yang F, Huang L, Zhang X (2014) Robust image hashing with dominant DCT coefficients. Optik-International Journal for Light and Electron Optics 125(18):5102–5107

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Tang Z, Ruan L, Qin C, Zhang X, Yud C (2015) Robust image hashing with embedding vector variance of LLE. Digital Signal Processing 43:17–27

    Article  Google Scholar 

  28. Tang Z, Huang L, Zhang X, Lao H (2016) Robust image hashing based on color vector angle and canny operator. Int J Electron Commun 70(6):833–841

    Article  Google Scholar 

  29. The USC-SIPI Image Database http://sipi.usc.edu/database/

  30. Wang J, Kumar S, Chang SF (2010) Semi-supervised hashing for scalable image retrieval. IEEE Conference on computer vision and pattern recognition, pp 3424-3431

  31. Weiss Y, Torralba A (1753-1760) Fergus R (2008) spectral hashing. In, Advances in Neural Information Processing Systems, New York, pp

    Google Scholar 

  32. Xiang S, Yang J (2012) Block-based image hashing with restricted blocking strategy for rotational robustness. EURASIP J Adv Signal Process 2012(77)

  33. Z-H Zhan, Zhang J, Li Y, Shi Y-H (2011) Orthogonal Learning Particle Swarm Optimization IEEE Transactions on Evolutionary Computation 15 (6): 832–847, .

  34. Zhang W, Ji J, Zhu J, Li J, Xu H, Zhang B (2016) BitHash: an efficient bitwise locality sensitive hashing method with applications. Knowl-Based Syst 97(7):40–47

    Article  Google Scholar 

  35. Zhao Y, Wang S, Zhang X, Yao H (2013) Robust hashing for image authentication using Zernike moments and local features. IEEE transactions on information forensics and security 8(1):55–63

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by National Natural Science Foundation of China (Grant No.61171109), Applied Basic Research Programs of Sichuan Science and Technology Department (Grant No.2014JY0215) and Basic Research Plan in SWUST (Grant No.13zx9101). The authors greatly thank the anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Qiang Ma, Ling Xing & Bin Wu

  2. Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang, 621900, China

    Qiang Ma

  3. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Lei Xu

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  1. Qiang Ma
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  2. Lei Xu
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  3. Ling Xing
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  4. Bin Wu
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Correspondence to Qiang Ma.

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Ma, Q., Xu, L., Xing, L. et al. Robust image authentication via locality sensitive hashing with core alignment. Multimed Tools Appl 77, 7131–7152 (2018). https://doi.org/10.1007/s11042-017-4625-x

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  • DOI: https://doi.org/10.1007/s11042-017-4625-x

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