Skip to main content
Log in

A video forgery detection algorithm based on compressive sensing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Video processing software is often used to delete moving objects and modify the forged regions with the information provided by the areas around them. However, few algorithms have been suggested for detecting this form of tampering. In this paper, a novel algorithm based on compressive sensing is proposed for the detection in which the moving foreground was removed from background. Firstly, the features of the difference between frames are obtained through K-SVD (k-Singular Value Decomposition), and then random projection is used to project the features into the lower-dimensional subspace which is clustered by k-means, and finally the detection results are combined to output. The experimental results show that our algorithm has higher detection accuracy and better robustness than that of the previous algorithms.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Aharon M, Elad M, Bruckstein A (2005) K-SVD: design of dictionaries for sparse representation

  2. Baraniuk RG (2007) Compressive sensing [lecture notes]. IEEE Signal Proc Mag 24(4):118–121

    Article  Google Scholar 

  3. Bo L, Ren X, Fox D (2011) Hierarchical matching pursuit for image classification: Architecture and fast algorithms. In: Advances in neural information processing systems. pp 2115–2123

  4. Candes T (2006) Near optimal signal recovery from random projections: universal encoding strategies. IEEE Trans Inf Theory 52(12):5406–5425

    Article  MathSciNet  Google Scholar 

  5. Candès EJ (2006) Compressive sampling. In: Proceedings oh the International Congress of Mathematicians: Madrid, August 22–30, 2006: invited lectures. pp 1433–1452

  6. Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  MATH  Google Scholar 

  7. Candes E, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 5(2):21–23

    Article  Google Scholar 

  8. Chambolle A, Lions P-L (1997) Image recovery via total variation minimization and related problems. Numer Math 76(2):167–188

    Article  MATH  MathSciNet  Google Scholar 

  9. Delp E, Memon N, Wu M (2009) Digital forensics [from the guest editors]. IEEE Signal Proc Mag 26(2):14–15

    Article  Google Scholar 

  10. Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  Google Scholar 

  11. Elad M, Bruckstein AM (2002) A generalized uncertainty principle and sparse representation in pairs of bases. IEEE Trans Inf Theory 48(9):2558–2567

    Article  MATH  MathSciNet  Google Scholar 

  12. Feng JZ, Song L, Yang XK, Zhang W (2009) Sub clustering K-SVD: Size variable dictionary learning for sparse representations. In: Image Processing (ICIP), 2009 16th IEEE International Conference on. IEEE, pp 2149–2152

  13. Figueiredo MA, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J SeL Top Sign Process 1(4)

  14. Hsu C-C, Hung T-Y, Lin C-W, Hsu C-T Video forgery detection using correlation of noise residue. In: Multimedia signal processing, 2008 I.E. 10th workshop on, 2008. IEEE, pp 170–174

  15. Iwen MA (2008) A deterministic sub-linear time sparse fourier algorithm via non-adaptive compressed sensing methods. In: Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms. Society for industrial and applied mathematics, pp 20–29

  16. Jähne B (2002) Digital image processing. Meas Sci Technol 13(9):1503

    Article  Google Scholar 

  17. Kobayashi M, Okabe T, Sato Y (2009) Detecting video forgeries based on noise characteristics. In: Advances in image and video technology. Springer, pp 306–317

  18. Mohimani H, Babaie-Zadeh M, Gorodnitsky I, Jutten C (2010) Sparse recovery using smoothed l 0 (SL0): Convergence analysis. arXiv preprint arXiv:10015073

  19. Mohimani H, Babaie-Zadeh M, Jutten C (2008) A fast approach for overcomplete sparse decomposition based on smoothed L0 norm. arXiv preprint arXiv:08092508

  20. Sarvotham S, Baron D, Baraniuk RG (2006) Compressed sensing reconstruction via belief propagation. preprint

  21. Shih TK, Tang NC, Hwang J-N (2007) Ghost shadow removal in multi-layered video inpaintinga. In: Multimedia and Expo, 2007 I.E. International Conference on. IEEE, pp 1471–1474

  22. Song Y (2011) Digital video forensics algorithm based on spatial and temporal matching. Tianjing University, Tianjing

    Google Scholar 

  23. Subramanyam A, Emmanuel S Video forgery detection using HOG features and compression properties. In: Multimedia Signal Processing (MMSP), 2012 I.E. 14th International Workshop on, 2012. IEEE, pp 89–94

  24. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666

    Article  MATH  MathSciNet  Google Scholar 

  25. Tsaig Y, Donoho DL (2004) Extensions of compressed sensing

  26. Wang W, Farid H (2007) Exposing digital forgeries in video by detecting duplication. In: Proceedings of the 9th workshop on multimedia & security. ACM, pp 35–42

  27. Wang W, Farid H (2007) Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans Inf Forensic Secur 2(3):438–449

    Article  Google Scholar 

  28. Wang W, Farid H (2009) Exposing digital forgeries in video by detecting double quantization. In: Proceedings of the 11th ACM workshop on multimedia and security. ACM, pp 39–48

  29. Young IT, Gerbrands JJ, Van Vliet LJ, Delft T (1998) Fundamentals of image processing. Delft University of Technology The Netherlands

  30. Zhang J, Su Y, Zhang M (2009) Exposing digital video forgery by ghost shadow artifact. In: Proceedings of the First ACM workshop on Multimedia in forensics. ACM, pp 49–54

Download references

Acknowledgments

This work was supported by the National Science Foundation of China (Grant No.61070062), Industry-university Cooperation Major Projects in Fujian Province (Grant No.2012H6006), Program for New Century Excellent Talents in University in Fujian Province (Grant No.JAI1038), the University Services HaiXi Major Project in Fujian Province (Grant No.2008HX200941-4-5), Science and Technology Department of Fujian province K-class Foundation Project (Grant No.JA10064), The Education Department of Fujian province A-class Foundation Project (Grant No.JA10064).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lichao Su.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Su, L., Huang, T. & Yang, J. A video forgery detection algorithm based on compressive sensing. Multimed Tools Appl 74, 6641–6656 (2015). https://doi.org/10.1007/s11042-014-1915-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1915-4

Keywords

Navigation