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Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations

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Abstract

Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness.

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References

  1. https://www.petitcolas.net/watermarking/image_database/, Accessed: 2019 June

  2. https://homepages.cae.wisc.edu/~ece533/images/, Accessed: 2019 June

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

    Article  Google Scholar 

  4. Alcantarilla P F, Bartoli A, Davison A J (2012) Kaze features. In: European conference on computer vision. Springer, pp 214–227

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

    Article  Google Scholar 

  6. Christoph Z (2010) Implementation and benchmarking of perceptual image hash functions. In: Upper Austria University of Applied Sciences. Hagenberg Campus

  7. Du L, Chen Z, Ho ATS (2020) Binary multi-view perceptual hashing for image authentication. Multimed Tools Appl:1–23

  8. Fridrich J, Goljan M (2000) Robust hash functions for digital watermarking. In: International conference on information technology: coding and computing. IEEE, pp 178–183

  9. Gharde N D, Thounaojam D M, Soni B, Biswas S K (2018) Robust perceptual image hashing using fuzzy color histogram. Multimed Tools Appl 77(23):30815–30840

    Article  Google Scholar 

  10. Hamid H, Ahmed F, Ahmad J (2020) Robust image hashing scheme using laplacian pyramids. Comput Electr Eng 84:106648

    Article  Google Scholar 

  11. Harris C G, Stephens M, et al. (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15. Citeseer, pp 10–5244

  12. Hassan E, Chaudhury S, Gopal M (2012) Feature combination in kernel space for distance based image hashing. IEEE Trans Multimed 14(4):1179–1195

    Article  Google Scholar 

  13. Hernandez R A P, Miyatake M N, Kurkoski B M (2011) Robust image hashing using image normalization and svd decomposition. In: International midwest symposium on circuits and systems. IEEE, pp 1–4

  14. Hong W, Chang Y-T, Qin H, Hung W-C, Tsai Y-H, Yang M-H (2020) Image hashing via linear discriminant learning. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 2531–2539

  15. Huang Z, Liu S (2020) Perceptual image hashing with texture and invariant vector distance for copy detection. IEEE Transactions on Multimedia

  16. Jie Z (2013) A novel block-dct and pca based image perceptual hashing algorithm. arXiv:1306.4079

  17. Karsh R K, Saikia A, Laskar R H (2018) Image authentication based on robust image hashing with geometric correction. Multimed Tools Appl 77 (19):25409–25429

    Article  Google Scholar 

  18. Khelaifi F, He H (2020) Perceptual image hashing based on structural fractal features of image coding and ring partition. Multimed Tools Appl:1–20

  19. Lefebvre F, Macq B, Legat J-D (2002) Rash: Radon soft hash algorithm. In: European Signal Processing Conference. IEEE, pp 1–4

  20. Leutenegger S, Chli M, Siegwart R Y (2011) Brisk: Binary robust invariant scalable keypoints. In: International conference on computer vision. IEEE, pp 2548–2555

  21. Neelima A, Singh K M (2016) Perceptual hash function based on scale-invariant feature transform and singular value decomposition. Comput J 59 (9):1275–1281

    Article  MathSciNet  Google Scholar 

  22. Olmos A, Kingdom FAA (2004) A biologically inspired algorithm for the recovery of shading and reflectance images. Perception 33(12):1463–1473

    Article  Google Scholar 

  23. Patil V, Sarode T K (2019) Image hashing using dwt-cslbp. JCP 14(3):210–222

    Article  Google Scholar 

  24. Qin C, Hu Y, Yao H, Duan X, Gao L (2019) Perceptual image hashing based on weber local binary pattern and color angle representation. IEEE Access 7:45460–45471

    Article  Google Scholar 

  25. Rosten E, Drummond T (2005) Fusing points and lines for high performance tracking. In: International Conference on Computer Vision (ICCV’05) Volume 1, vol 2. IEEE, pp 1508–1515

  26. Sajjad M, Haq I U, Lloret J, Ding W, Muhammad K (2019) Robust image hashing based efficient authentication for smart industrial environment. IEEE Trans Ind Inf 15(12):6541–6550

    Article  Google Scholar 

  27. Singh K M, Neelima A, Tuithung T, Singh K M (2019) Robust perceptual image hashing using sift and svd. Curr Sci 117(8):1340

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Tang Z, Dai Y, Zhang X, Huang L, Yang F (2013) Robust image hashing via colour vector angles and discrete wavelet transform. IET Image Process 8(3):142–149

    Article  Google Scholar 

  30. Tang Z, Wang S, Zhang X, Wei W, Su S (2008) Robust image hashing for tamper detection using non-negative matrix factorization. J Ubiquit Conver Technol 2(1):pp–18

    Google Scholar 

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

    Article  Google Scholar 

  32. Tang Z, Zhang X, Dai X, Yang J, Wu T (2013) Robust image hash function using local color features. AEU-Int J Electron Commun 67 (8):717–722

    Article  Google Scholar 

  33. Tang Z, Zhang X, Huang L, Dai Y (2013) Robust image hashing using ring-based entropies. Signal Process 93(7):2061–2069

    Article  Google Scholar 

  34. Tang Z, Zhang X, Li X, Zhang S (2015) Robust image hashing with ring partition and invariant vector distance. IEEE Trans Inf Forensic Secur 11 (1):200–214

    Article  Google Scholar 

  35. Tang Z, Zhang X, Zhang S (2013) Robust perceptual image hashing based on ring partition and nmf. IEEE Trans Knowl Data Eng 26(3):711–724

    Article  MathSciNet  Google Scholar 

  36. Tang ZJ, Zhang XQ, Dai YM, Lan WW (2013) Perceptual image hashing using local entropies and dwt. The Imaging Sci J 61(2):241–251

    Article  Google Scholar 

  37. Wang X, Zhou X, Zhang Q, Xu B, Xue J (2020) Image alignment based perceptual image hash for content authentication. Signal Process Image Commun 80:115642

    Article  Google Scholar 

  38. Weber A G (2018) The usc-sipi image database version 6. USC-SIPI Report 432:1–24

    Google Scholar 

  39. Weng L, Preneel B (2009) Shape-based features for image hashing. In: International Conference on Multimedia and Expo. IEEE, pp 1074–1077

  40. Xiang S, Kim H-J, Huang J (2007) Histogram-based image hashing scheme robust against geometric deformations. In: Proceedings of the 9th workshop on Multimedia & security. ACM, pp 121–128

  41. Xue M, Yuan C, Liu Z, Wang J (2019) Ssl: A novel image hashing technique using sift keypoints with saliency detection and lbp feature extraction against combinatorial manipulations. Security and Communication Networks

  42. Yang B, Gu F, Niu X (2006) Block mean value based image perceptual hashing. In: International conference on intelligent information hiding and multimedia. IEEE, pp 167–172

  43. Zhao Y, Wang S, Zhang X, Yao H (2012) Robust hashing for image authentication using zernike moments and local features. IEEE Trans Inf Forensic Secur 8(1):55–63

    Article  Google Scholar 

  44. Zhao Y, Yuan X (2020) Perceptual image hashing based on color structure and intensity gradient. IEEE Access 8:26041–26053

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Silchar, Assam, India

    Moumita Roy, Dalton Meitei Thounaojam & Shyamosree Pal

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  1. Moumita Roy
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  2. Dalton Meitei Thounaojam
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  3. Shyamosree Pal
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Correspondence to Moumita Roy.

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Availability of Data and Material

The datasets used to support the findings of this study are available from the public repositories mentioned in the manuscript- [1, 2, 38]

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Roy, M., Thounaojam, D.M. & Pal, S. Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations. Multimed Tools Appl 81, 29045–29073 (2022). https://doi.org/10.1007/s11042-022-12626-4

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  • DOI: https://doi.org/10.1007/s11042-022-12626-4

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