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Robust video hashing based on representative-dispersive frames

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Abstract

This study proposes a robust video hashing for video copy detection. The proposed method, which is based on representative-dispersive frames (R-D frames), can reveal the global and local information of a video. In this method, a video is represented as a graph with frames as vertices. A similarity measure is proposed to calculate the weights between edges. To select R-D frames, the adjacency matrix of the generated graph is constructed, and the adjacency number of each vertex is calculated, and then some vertices that represent the R-D frames of the video are selected. To reveal the temporal and spatial information of the video, all R-D frames are scanned to constitute an image called video tomography image, the fourth-order cumulant of which is calculated to generate a hash sequence that can inherently describe the corresponding video. Experimental results show that the proposed video hashing is resistant to geometric attacks on frames and channel impairments on transmission.

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References

  1. Cox I, Kilian J, Leighton F, et al. Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process, 1997, 6: 1673–1687

    Article  Google Scholar 

  2. Hartung F, Kutter M. Multimedia watermarking techniques. Proc IEEE, 1999, 87: 1079–1107

    Article  Google Scholar 

  3. Joly A, Buisson O, Frelicot C. Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans Multimedia, 2007, 9: 293–306

    Article  Google Scholar 

  4. Lee S, Yoo C D. Video fingerprinting based on centroids of gradient orientations. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, 2006. 401–404

  5. Zhou X B, Schmucker M, Christopher L. Perceptual hashing of video content based on differential block similarity. In: Proceedings of the International Conference on Computational Intelligence and Security, Xi’an, 2005. 80–85

  6. Coskun B, Sankur B, Memon N. Spatio-temporal transform based video hashing. IEEE Trans Multimedia, 2006, 8: 1190–1208

    Article  Google Scholar 

  7. Esmaeili M M, Fatourechi M, Ward R K. A robust and fast video copy detection system using content-based fingerprinting. IEEE Trans Inf Forensic Secur, 2011, 6: 213–216

    Article  Google Scholar 

  8. Law T J, Chen L, Joly A, et al. Video copy detection: a comparative study. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, Amsterdam, 2007. 371–378

  9. Xiang S J, Yang J Q, Huang J W. Perceptual video hashing robust against geometric distortions. Sci China Inf Sci, 2012, 55: 1520–1527

    Article  MathSciNet  Google Scholar 

  10. Li W, Preneel B. From image hashing to video hashing. In: Proceedings of the 16th International Multimedia Modeling Conference, Chongqing, 2010. 662–668

  11. Nie X S, Liu J, Sun J D. Robust video hashing for identification based on MDS. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Dallas, 2010. 1834–1837

  12. Nie X S, Liu J, Sun J D, et al. Robust video hashing based on double-layer embedding. IEEE Signal Process Lett, 2011, 18: 307–310

    Article  Google Scholar 

  13. Wei Z K, Zhao Y, Zhu C, et al. Frame fusion for video copy detection. IEEE Trans Circuits Syst Video Technol, 2011, 21: 15–28

    Article  Google Scholar 

  14. Douze M, Jegou H, Schmid C. An image-based approach to video copy detection with spatio-temporal post-filtering. IEEE Trans Multimedia, 2010, 12: 257–266

    Article  Google Scholar 

  15. Sun J D, Wang J, Zhang J, et al. Video hashing algorithm with weighted matching based on visual saliency. IEEE Signal Process Lett, 2012, 19: 328–331

    Article  Google Scholar 

  16. Wang J, Sun J D, Liu J, et al. A visual saliency based video hashing algorithm. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), Orlando, 2012. 645–648

  17. Akutsu A, Tonomura Y. Video tomography: An efficient method for camerawork extraction and motion analysis. In: Proceedings of the ACM International Conference on Multimedia. New York: ACM, 1994. 349–356

    Google Scholar 

  18. Yu L J, Schmucker M, Busch C, et al. Cumulant-based image fingerprints. In: Proceedings of SPIE-Security, Steganography, and Watermarking of Multimedia Contents, San Jose, 2005. 68–75

  19. Varna A L, Swaminathan A, Wu M. A decision theoretic framework for analyzing binary hash-based content identification systems. In: Proceedings of the ACM Conference on Computer and Communications Security. New York: ACM, 2008. 67–76

    Google Scholar 

  20. Oostveen J C, Kalker T, Haitsma J. Visual hashing of digital video: Applications and techniques. In: Proceedings of SPIE—Storage and Retrieval for Image and Video Databases, San Diego, 2001. 121–131

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Authors and Affiliations

  1. School of Information Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    XiuShan Nie

  2. School of Information Science and Engineering, Shandong University, Jinan, 250100, China

    XiuShan Nie, Ju Liu, JianDe Sun & XiaoHui Yang

  3. Hisense State Key Laboratory of Digital Multi-Media Technology, Qingdao, 266071, China

    Ju Liu

  4. Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, 92697, USA

    LianQi Wang

Authors
  1. XiuShan Nie
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  2. Ju Liu
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  3. JianDe Sun
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  4. LianQi Wang
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  5. XiaoHui Yang
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Correspondence to Ju Liu.

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Nie, X., Liu, J., Sun, J. et al. Robust video hashing based on representative-dispersive frames. Sci. China Inf. Sci. 56, 1–11 (2013). https://doi.org/10.1007/s11432-012-4760-y

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  • DOI: https://doi.org/10.1007/s11432-012-4760-y

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