Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Video frame-rate up-conversion is one of the common operations for tampering digital videos in the temporal domain, such as creating fake high-quality videos and splicing two video clips with different frame rates. However, few existing works have been proposed for detecting this form of tampering operation. Based on the analysis of extensive experiments, we found that frame-rate up-conversion algorithms employed in most current video editing softwares will inevitably introduce some periodic artifacts into inter-frame similarity in the resulting video frame sequence. By analyzing such artifacts, we propose a simple yet very effective method to expose video after frame-rate up-conversion, and further estimate its original frame rate. The experimental results evaluated on 100 original videos at different frame rates have shown the effectiveness of the proposed method. The average detection accuracy can achieve as high as 99 % on noise-free videos in uncompressed and H.264/AVC formats. Besides, the proposed method is robust to noise as the detection accuracy could reach over 85 % and 95 % on noised videos with Gaussian white noise when SNR is 33 db and 36 db respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. The test video clips are coming from the public website http://trace.eas.asu.edu/yuv/ and AVS Workgroup (http://www.avs.org.cn/).

References

  1. Bestagini P, Allam A, Milani S, Tagliasacchi M, Tubaro S (2012) Video codec identification. In: Proceedings of ICASSP, pp 2257–2260

  2. Choi BT, Lee SH, Ko SJ (2000) New frame rate up-conversion using bi-directional motion estimation. IEEE Trans Consum Electron 46:603–609

    Article  Google Scholar 

  3. Choi BD, Han JW, Kim CS, Ko SJ (2007) Motion-compensated frame interpolation using bilateral motion estimation and adaptive overlapped block motion compensation. IEEE Trans Circuits Syst Video Technol 17:407–416

    Article  Google Scholar 

  4. Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16–25

    Article  Google Scholar 

  5. Gallagher AC (2005) Detection of linear and cubic interpolation in JPEG compressed images. In: Proceedings of 2nd Canadian conference on computer and robot vision, pp 65–72

  6. Hsu CC, Hung TY, Lin CW, Hsu CT (2008) Video forgery detection using correlation of noise residue. In: Proceedings of IEEE 10th workshop multimedia signal processing, pp 170–174

  7. Huang HC, Chang FC (2013) Hierarchy-based reversible data hiding. Expert Syst Appl 40(1):34–43

    Article  Google Scholar 

  8. Huang HC, Fang WC (2010) Metadata-based image watermarking for copyright protection. Simulation Modelling Practice and Theory 18(4):436–445

    Article  Google Scholar 

  9. Kobayashi M, Okabe T, Sato Y (2010) Detecting forgery from static-scene video based on inconsistency in noise level functions. IEEE Trans Inf Foren Sec 5(4):883–892

    Article  Google Scholar 

  10. Liao D, Yang R, Liu H, Li J, Huang J (2011) Double h.264/avc compression detection using quantized nonzero ac coefficients. Proc. SPIE 7880:78800Q–78800Q-10

    Google Scholar 

  11. Luo W, Qu Z, Pan F, Huang J (2007) A survey of passive technology for digital image forensics. Front Comput Sci Chin 1(2):166–179

    Article  Google Scholar 

  12. Luo W, Wu M, Huang J (2008) Mpeg recompression detection based on block artifacts. Proc SPIE 6819:68190X–68190-12

    Google Scholar 

  13. Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Foren Sec 3:529–538

    Article  Google Scholar 

  14. Ritchey PC, Rego VJ (2012) A context sensitive tiling system for information hiding. JIH–MSP 3(3):212–226

    Google Scholar 

  15. Software (2011) Available on http://www.imtoo.com/video-converter.html. Accessed Dec 2011

  16. Software (2011) Available on http://www.avs4you.com/AVS-Video-Converter.aspx. Accessed Dec 2011

  17. Software (2011) Available on http://www.any-video-converter.com/. Accessed Dec 2011

  18. Softwares (2011) Available on http://video-converter-software-review.toptenreviews.com/. Accessed Nov 2011

  19. Stamm MC, Lin WS, Liu KJR (2012) Temporal forensics and anti-forensics for motion compensated video. IEEE Trans Inf Foren Sec 7(4):1315–1329

    Article  Google Scholar 

  20. Wang W, Farid H (2006) Exposing digital forgeries in video by detecting double mpeg compression. In: Proceedings of ACM 8th workshop on multimedia and security, pp 37–47

  21. Wang W, Farid H (2007) Exposing digital forgeries in interlaced and deinterlaced video. IEEE Trans Inf Foren Sec 2:438–449

    Article  Google Scholar 

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

  23. Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612

    Article  Google Scholar 

  24. Weng S, Pan JS, Gao X (2012) Reversible watermark combining pre-processing operation and histogram shifting. JJIH–MSP 3(4):320–326

    Google Scholar 

  25. Yang R, Shi YQ, Huang J (2009) Defeating fake-quality mp3. In: Proceedings of ACM 11th workshop on multimedia and security. NY, USA, pp 117–124

Download references

Acknowledgements

This work is supported by the 973 Program (2011CB302204), NSFC (61272191,61003243,61173081), Zhujiang Science & technology (2011J2200091), and Guangdong Natural Science Foundation (S2011020001215).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, 510006, People’s Republic of China

    Shan Bian & Jiwu Huang

  2. School of Software, Sun Yat-sen University, Ghuangzhou, 510006, People’s Republic of China

    Weiqi Luo

Authors
  1. Shan Bian
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Weiqi Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiwu Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Weiqi Luo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bian, S., Luo, W. & Huang, J. Detecting video frame-rate up-conversion based on periodic properties of inter-frame similarity. Multimed Tools Appl 72, 437–451 (2014). https://doi.org/10.1007/s11042-013-1364-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1364-5

Keywords