Skip to main content
SpringerLink
Log in

High Accuracy Perceptual Video Hashing via Low-Rank Decomposition and DWT

  • Conference paper
  • First Online:
MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

Included in the following conference series:

Abstract

In this work, we propose a novel robust video hashing algorithm with High Accuracy. The proposed algorithm generates a fix-up hash via low-rank and sparse decomposition and discrete wavelet transform (DWT). Specifically, input video is converted to randomly normalized video with logistic map, and then content-based feature matrices extract from a randomly normalized video with low-rank and sparse decomposition. Finally, data compression with 2D-DWT of LL sub-band is applied to feature matrices and statistic properties of DWT coefficients are quantized to derive a compact video hash. Experiments with 4760 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons indicate that the proposed video hashing more desirable performance than some algorithms in classification between robustness and discrimination.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ye, D., et al.: Improved ordinary measure and image entropy theory based intelligent copy detection method. Int. J. Comput. Intell. Syst. 4(5), 777–787 (2011)

    Article  Google Scholar 

  2. Qin, C., Chang, C., Chiu, Y.: A novel joint data-hiding and compression scheme based on SMVQ and image inpainting. IEEE Trans. Image Process. 23(3), 969–978 (2014)

    Article  MathSciNet  Google Scholar 

  3. Ye, D., et al.: Scalable content authentication in H.264/SVC video using perceptual hashing based on Dempster-Shafer theory. Int. J. Comput. Intell. Syst. 5(5), 953–963 (2012)

    Article  Google Scholar 

  4. Qin, C., et al.: Separable reversible data hiding in encrypted images via adaptive embedding strategy with block selection. Sig. Process. 153, 109–122 (2018)

    Article  Google Scholar 

  5. Yang, G., Chen, N., Jiang, Q.: A robust hashing algorithm based on SURF for video copy detection. Comput. Secur. 31(1), 33–39 (2012)

    Article  Google Scholar 

  6. Li, M., Monga, V.: Robust video hashing via multilinear subspace projections. IEEE Trans. Image Process. 21(10), 4397–4409 (2012)

    Article  MathSciNet  Google Scholar 

  7. Saikia, N.: Perceptual hashing in the 3D-DWT domain. In: Proceedings of International Conference on Green Computing and Internet of Things, pp. 694–698 (2015)

    Google Scholar 

  8. Setyawan, I., Timotius, I.K.: Spatio-temporal digital video hashing using edge orientation histogram and discrete cosine transform. In: Proceedings of International Conference on Information Technology Systems and Innovation (ICITSI), pp. 24–27 (2015)

    Google Scholar 

  9. Chen, H., Yan, W., Han, G.: Multi-granularity geometrically robust video hashing for tampering detection. Multimed. Tools Appl. 77(5), 5303–5321 (2017)

    Article  Google Scholar 

  10. Nie, X., et al.: Spherical torus-based video hashing for near-duplicate video detection. Sci. China Inf. Sci. 59(5), 1–3 (2016)

    Article  Google Scholar 

  11. Sandeep, R., Saksham, S., Prabin, K.B.: Perceptual video hashing using 3D-radial projection technique. In: Proceedings of International Conference on Signal Processing, Communication and Networking, pp. 1–6 (2017)

    Google Scholar 

  12. Tang, Z., et al.: Video hashing with DCT and NMF. Comput. J., Oxford (2019). https://doi.org/10.1093/comjnl/bxz060

  13. Pareek, N.K., Patidar, V., Sud, K.K.: Image encryption using chaotic logistic map. Image Vis. Comput. 24(9), 926–934 (2006)

    Article  Google Scholar 

  14. Liu, Y., et al.: Structure-constrained low-rank and partial sparse representation for image classification. In: Proceedings of International Conference on Image Processing, Communication and Networking, pp. 5222–5226 (2014)

    Google Scholar 

  15. Xiong, F., Zhou, J., Qian, Y.: Hyperspectral imagery denoising via reweighed sparse low-rank nonnegative tensor factorization. In: Proceedings of International Conference on Image Processing, pp. 3219–3223 (2018)

    Google Scholar 

  16. Lin, Z., et al.: The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC, UILUENG-09-2215 (2009)

    Google Scholar 

  17. Tang, Z., et al.: Perceptual image hashing with weighted DWT features for reduced-reference image quality assessment. Comput. J. 61(11), 1695–1709 (2018)

    Article  Google Scholar 

  18. Tang, Z., et al.: Robust image hashing with tensor decomposition. IEEE Trans. Knowl. Data Eng. 31(3), 549–560 (2019)

    Article  Google Scholar 

  19. The open video project. http://www.open-video.org/. Accessed 12 May 2018

  20. ReefVid: Free Reef Video Clip Database. http://www.reefvid.org/. Accessed 16 July 2018

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China NSFC (U1636101, U1736211, U1636219), the National Key Research Development Program of China (2016QY01W0200).

Author information

Authors and Affiliations

  1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, Hubei, China

    Lv Chen, Dengpan Ye & Shunzhi Jiang

Authors
  1. Lv Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dengpan Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shunzhi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lv Chen .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, L., Ye, D., Jiang, S. (2020). High Accuracy Perceptual Video Hashing via Low-Rank Decomposition and DWT. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37731-1_65

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37730-4

  • Online ISBN: 978-3-030-37731-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation