skip to main content
research-article

Efficient Image Hashing with Geometric Invariant Vector Distance for Copy Detection

Authors Info & Claims
Published:16 December 2019Publication History
Skip Abstract Section

Abstract

Hashing method is an efficient technique of multimedia security for content protection. It maps an image into a content-based compact code for denoting the image itself. While most existing algorithms focus on improving the classification between robustness and discrimination, little attention has been paid to geometric invariance under normal digital operations, and therefore results in quite fragile to geometric distortion when applied in image copy detection. In this article, a novel effective image hashing method is proposed based on geometric invariant vector distance in both spatial domain and frequency domain. First, the image is preprocessed by some joint operations to extract robust features. Then, the preprocessed image is randomly divided into several overlapping blocks under a secret key, and two different feature matrices are separately obtained in the spatial domain and frequency domain through invariant moment and low frequency discrete cosine transform coefficients. Furthermore, the invariant distances between vectors in feature matrices are calculated and quantified to form a compact hash code. We conduct various experiments to demonstrate that the proposed hashing not only reaches good classification between robustness and discrimination, but also resists most geometric distortion in image copy detection. In addition, both receiver operating characteristics curve comparisons and mean average precision in copy detection clearly illustrate that the proposed hashing method outperforms state-of-the-art algorithms.

References

  1. R. Venkatesan, S.-M. Koon, M. H. Akubowski, and P. Moulin. 2000. A new approach to image copy detection based on extended feature sets. In Proceedings of the IEEE International Conference on Image Processing, Vol. 3, 664--666.Google ScholarGoogle Scholar
  2. A. Swaminathan, Y. Mao, and M. Wu. 2006. Robust and secure image hashing. IEEE Trans. Inform. Forens. Secur. 1, 2 (2006), 215--230.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Z. Tang, X. Zhang, X. Li, and S. Zhang. 2016. Robust image hashing with ring partition and invariant vector distance. IEEE Trans. Inform. Forens. Secur. 11, 1 (2016), 200--214.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C. De Roover, C. De Vleeschouwer, F. Lefebvre, and B. Macq. 2005. Robust video hashing based on radial projections of key frames. IEEE Trans. Sig. Proc. 53, 10 (2005), 4020--4037.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. X. Lv and Z. Wang. 2012. Perceptual image hashing based on shape contexts and local feature points. IEEE Trans. Inform. Forens. Secur. 7, 3 (2012), 1081--1093.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. F. Khelif and J. Jiang. 2010. Perceptual image hashing based on virtual watermark detection. IEEE Trans. Image Proc. 19, 4 (2010), 981--994.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. Li, Z. Lu, C. Zhu, and X. Niu. 2012. Robust image hashing based on random Gabor filtering and dithered lattice vector quantization. IEEE Trans. Image Proc. 21, 4 (2012), 1963--1980.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. F. Shen, Y. Yang, L. Liu, W. Liu, and D. Tao. 2017. Asymmetric binary coding for image search. Asymm. Bin. Cod. Image Search 19, 9 (2017), 2022--2032.Google ScholarGoogle Scholar
  9. A. Menezes, V. Oorschot, and S. Vanstone. 1998. Handbook of Applied Cryptography. CRC Press, 683--683.Google ScholarGoogle Scholar
  10. S. Manuel. 2011. Classification and generation of disturbance vectors for collision attacks against SHA-1. Desi., Codes Cryptog. 59, 2 (2011), 247--263.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. F. Balado, N. Hurley, E. McCarthy, and G. Silvestre. 2007. Performance analysis of robust audio hashing. IEEE Trans. Inform. Forens. Secur. 2, 2 (2007), 254--266.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Z. Tang, X. Zhang, and S. Zhang. 2014. Robust perceptual image hashing based on ring partition and NMF. IEEE Trans. Knowl. Data Eng. 26, 3 (2014), 711--724.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. F. Ahmed, M. Y. Siyal, and V. U. Abbas. 2010. A secure and robust hash based scheme for image authentication. Sig. Proc. 90, 5 (2010), 1456--1470.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. X. Wang, K. Pang, X. Zhou, Y. Zhou, L. Li, and J. Xue. 2015. A visual model-based perceptual image hash for content authentication. IEEE Trans. Inform. Forens. Secur. 10, 7 (2015), 1336--1349.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K. Li, G. J. Qi, and K. A. Hua. 2018. Learning label preserving binary codes for multimedia retrieval: A general approach. ACM Trans. Multimedia Comput. Commun. Applic. 14, 1 (2018), 2:1--2:23.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. L. Yang, G. Bo, A. Hanjalic, and X. Hua. 2012. A unified context model for web image retrieval. ACM Trans. Multimedia Comput. Commun. Applic. 8, 3 (2012), 28:1--28:19.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. R. Li, B. Bhanu, and A. Dong. 2008. Feature synthesized EM algorithm for image retrieval. ACM Trans. Multimedia Comput. Commun. Applic. 4, 2 (2008), 10:1--10:24.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Z. Tang, H. Lao, X. Zhang, and K. Liu. 2016. Robust image hashing via DCT and LLE. Comput. Secur. 62 (2016), 133--148.Google ScholarGoogle ScholarCross RefCross Ref
  19. C. Qin and X. Chen. 2018. Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inform. Sci. 423 (2018), 284--302.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Z. Tang, Z. Huang, X. Zhang, and H. Lao. 2017. Robust image hashing with multidimensional scaling. Sig. Proc. 137 (2017), 240--250.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Y. Ou and K. H. Rhee. 2009. A key-dependent secure image hashing scheme by using Radon transform. In Proceedings of the IEEE International Symposium on Intelligent Signal Processing and Communication Systems. 595--598.Google ScholarGoogle Scholar
  22. Z. Tang, L. Huang, X. Zhang, and H. Lao. 2016. Robust image hashing based on color vector angle and Canny operator. AEU-Int. J. Electron. Commun. 70, 6 (2016), 833--841.Google ScholarGoogle ScholarCross RefCross Ref
  23. S. S. Kozat, R. Venkatesan, and M. K. Mihcak. 2004. Robust perceptual image hashing via matrix invariants. In Proceedings of the IEEE International Conference on Image Processing, Vol. 5, 3443--3446.Google ScholarGoogle Scholar
  24. V. Monga and M. K. Mihcak. 2007. Robust and secure image hashing via non-negative matrix factorizations. IEEE Trans. Inform. Forens. Secur. 2, 3 (2007), 376--390.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. R. Davarzani, S. Mozaffariand, and K. Yaghmaie. 2016. Perceptual image hashing using center-symmetric local binary patterns. Multimedia Tools Applic. 75, 8 (2016), 4639--4667.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Z. Tang, L. Chen, X. Zhang, and S. Zhang. 2019. Robust image hashing with tensor decomposition. IEEE Trans. Knowl. Data Eng. 31, 3 (2019), 549--560.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Y. Zhao, S. Wang, X. Zhang, and H. Yao. 2013. Robust hashing for image authentication using Zernike moments and local features. IEEE Trans. Inform. Forens. Secur. 8, 1 (2013), 55--63.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. F. Lefebvre, B. Macq, and J. D. Legat. 2002. RASH: Radon soft hash algorithm. In Proceedings of the European Signal Processing Conference. 299--302.Google ScholarGoogle Scholar
  29. D. Nguyen, W. Li, and B. Preneel. 2011. Radon transform-based secure image hashing. In Proceedings of the International Conference on Communications and Multimedia Security, Vol. 7025, 186--193.Google ScholarGoogle Scholar
  30. Y. Liu, G. Xin, and Y. Xiao. 2016. Robust image hashing using Radon transform and invariant features. Radioengineering 25, 3 (2016), 556--564.Google ScholarGoogle ScholarCross RefCross Ref
  31. G. Wolberg and S. Zokai. 2002. Robust image registration using log-polar transform. In Proceedings of the International Conference on Image Processing, Vol. 1, 493--496.Google ScholarGoogle Scholar
  32. Z. Tang, L. Ruan, C. Qin, X. Zhang, and C. Yu. 2015. Robust image hashing with embedding vector variance of LLE. Dig. Sig. Proc. 53 (2015), 17--27.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. X. Zhu, X. Li, S. Zhang, Z. Xu, L. Yu, and C. Wang. 2017. Graph PCA Hashing for Similarity Search. IEEE Trans. Multimedia 19, 9 (2017), 2033--2044.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. S. Xiang. 2010. Histogram-based perceptual image hashing in the DWT domain. In Proceedings of the International Conference on Multimedia Information Networking and Security, 653--657.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Z. Tang, Y. Dai, X. Zhang, L. Huang, and F. Yang. 2014. Robust image hashing via colour vector angles and discrete wavelet transform. IET Image Proc. 8, 3 (2014), 142--149.Google ScholarGoogle ScholarCross RefCross Ref
  36. C. Lin and S. Chang. 2001. A robust image authentication method distinguishing JPEG compression from malicious manipulation. IEEE Trans. Circ. Syst. Vid. Technol. 11, 2 (2001), 153--168.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Z. Tang, F. Yang, L. Huang, and X. Zhang. 2014. Robust image hashing with dominant DCT coefficients. Optik-Int. J. Light Electron Optics 125, 18 (2014), 5102--5107.Google ScholarGoogle ScholarCross RefCross Ref
  38. Z. Tang, X. Li, X. Zhang, S. Zhang, and Y. Dai. 2018. Image hashing with color vector angle. Neurocomputing 308 (2018), 147--158.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. C. Yan, C. Pun, and X. Yuan. 2016. Quaternion-based image hashing for adaptive tampering localization. IEEE Trans. Inform. Forens. Secur. 11, 12 (2016), 2664--2677.Google ScholarGoogle ScholarCross RefCross Ref
  40. C. Yan, C. Pun, and X. Yuan. 2016. Multi-scale image hashing using adaptive local feature extraction for robust tampering detection. Sig. Proc. 121 (2016), 1--16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. X. Lv and Z. Wang. 2009. An extended image hashing concept: Content-based fingerprinting using FJLT. EURASIP J. Inform. Sec. 1 (2009), 1--16.Google ScholarGoogle ScholarCross RefCross Ref
  42. J. Song, H. Zhang, X. Li, L. Gao, M. Wang, and R. Hong. 2018. Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans. Image Proc. 27, 7 (2018), 3210--3221.Google ScholarGoogle ScholarCross RefCross Ref
  43. J. Song, L. Gao, L. Liu, X. Zhu, and N. Sebe. 2018. Quantization-based hashing: A general framework for scalable image and video retrieval. Pattern Recog. 75 (2018), 175--187.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. X. Liu, Z. Li, C. Deng, and D. Tao. 2017. Distributed adaptive binary quantization for fast nearest neighbor search. IEEE Trans. Image Proc. 26, 11 (2017), 5324--5336.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. W. Kang, W. Li, and Z. Zhou. 2016. Column sampling based discrete supervised hashing. In Proceedings of the AAAI Conference on Artificial Intelligence. 1230--1236.Google ScholarGoogle Scholar
  46. X. Wang, L. Gao, P. Wang, X. Sun, and X. Liu. 2018. Two-stream 3D convNet fusion for action recognition in videos with arbitrary size and length. IEEE Trans. Multimedia 20, 3 (2018), 634--644.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. X. Wang, L. Gao, J. Song, and H. Shen. 2017. Beyond frame-level CNN: Saliency-aware 3D CNN with LSTM for video action recognition. IEEE Sig. Proc. Lett. 24, 4 (2017), 510--514.Google ScholarGoogle ScholarCross RefCross Ref
  48. F. Shen, X. Gao, L. Liu, Y. Yang, and H. Shen. 2017. Deep asymmetric pairwise hashing. In Proceedings of the ACM International Conference on Multimedia. 1522--1530.Google ScholarGoogle Scholar
  49. C. Deng, Z. Chen, X. Liu, X. Gao, and D. Tao. 2018. Triplet-based deep hashing network for cross-modal retrieval. IEEE Trans. Image Proc. 27, 8 (2018), 3893--3903.Google ScholarGoogle ScholarCross RefCross Ref
  50. Z. Tang, X. Li, J. Song, M. Wei, and X. Zhang. 2015. Colour space selection in image hashing: An experimental study. IETE Tech. Rev. 34, 4 (2017), 440--447.Google ScholarGoogle ScholarCross RefCross Ref
  51. R. C. Gonzalez and R. E. Woods. 2007. Digital Image Processing (3rd ed.). Prentice Hall.Google ScholarGoogle Scholar
  52. M. K. Hu. 1962. Visual pattern recognition by moment invariants. IRE Trans. Inform. Theor. 8, 2 (1962), 179--187.Google ScholarGoogle ScholarCross RefCross Ref
  53. Usc-Sipi Image Database. Retrieved from: http://sipi.usc.edu/database/.Google ScholarGoogle Scholar
  54. H. Jegou, F. Perronnin, M. Douze, J. Nchez, P. Perez, and C. Schmid. 2012. Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Machine Intell. 34, 9 (2012), 1704--1716.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Ground Truth Database. Retrieved from: http://www.cs.washington.edu/reseach/Imagedatabase/groundtruth/.Google ScholarGoogle Scholar
  56. F. A. P. Petitcolas. 2000. Watermarking schemes evaluation. IEEE Sig. Proc. Mag. 17, 5 (2000), 58--64.Google ScholarGoogle ScholarCross RefCross Ref
  57. X. Huang, X. Liu, G. Wang, and M. Su. 2016. A robust image hashing with enhanced randomness by using random walk on zigzag blocking. In Proceedings of the IEEE Trustcom/BigDataSE/ISPA Conference. 14--18.Google ScholarGoogle Scholar
  58. J. Wang, G. Wiederhold, O. Firschein, and S. Wei. 1998. Content-based image indexing and searching using Daubechies’ wavelets. Int. J. Dig. Lib. 1, 4 (1998), 311--328.Google ScholarGoogle ScholarCross RefCross Ref
  59. A. Turpin and F. Scholer. 2006. User performance versus precision measures for simple search tasks. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 11--18.Google ScholarGoogle Scholar
  60. Z. Zhou, Y. Wang, Q. Wu, C. Yang, and X. Sun. 2017. Effective and efficient global context verification for image copy detection. IEEE Trans. Inform. Forens. Secur. 12, 1 (2017), 48--63.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. J. Hsiao, C. Chen, L. Chien, and M. Chen. 2007. A new approach to image copy detection based on extended feature sets. IEEE Trans. Image Proc. 16, 8 (2007), 2069--2079.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. H. Ling, H. Cheng, Q. Ma, F. Zou, and W. Yan. 2012. Efficient image copy detection using multiscale fingerprints. IEEE Multimedia 19, 1 (2012), 60--69.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. C. I. Podilchuk and E. J. Delp. 2001. Digital watermarking: Algorithms and applications. IEEE Sig. Proc. Mag. 18, 4 (2001), 33--46.Google ScholarGoogle ScholarCross RefCross Ref
  64. C. Kim and Gordo. 2003. Content-based image copy detection. Sig. Proc. Image Commun. 18, 3 (2003), 169--184.Google ScholarGoogle ScholarCross RefCross Ref
  65. A. Gordo, F. Perronnin, Y. Gong, and S. Lazebnik. 2014. Asymmetric distances for binary embeddings. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1 (2014), 33--47.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Efficient Image Hashing with Geometric Invariant Vector Distance for Copy Detection

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!