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Video Tampering Detection for Decentralized Video Transcoding Networks

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Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12131))

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Abstract

This paper introduces a complete methodology based on Machine Learning and Computer Vision techniques for the verification of video transcoding computations in decentralized networks, particularly the Open Source project Livepeer. A base video dataset is presented, with over 180k samples transcoded using the x264 codec. As a novelty, we propose a set of four features computed as a full reference comparison between the source and the rendered videos. Using these features, a One Class Support Vector Machine is trained to identify good encodings with a high accuracy. Experimental results are presented and the particular constraints of this use case are explained.

Supported by Livepeer and Haivision.

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References

  1. Aggarwal, R.D.S.N.: Video content authentication techniques: a comprehensive survey. Multimedia Syst. 24(2), 211–240 (2017). https://doi.org/10.1007/s00530-017-0538-9

  2. Aiyer, A.S., Alvisi, L., Clement, A., Dahlin, M., Martin, J.P., Porth, C.: BAR fault tolerance for cooperative services. ACM SIGOPS Oper. Syst. Rev. 39(5), 45 (2005). https://doi.org/10.1145/1095809.1095816

  3. Bampis, C.G., Bovik, A.C.: Feature-based prediction of streaming video QoE: distortions stalling and memory. Sig. Process.: Image Commun. 68, 218–228 (2018). https://doi.org/10.1016/j.image.2018.05.017

  4. Crowcroft, J., Moreton, T.D., Pratt, I., Twigg, A.: Peer-to-peer systems and the grid. In: The Grid 2e: Blueprint for a New Computing Infrastructure. University of Cambridge Computer Laboratory (2003)

    Google Scholar 

  5. Duanmu, Z., Rehman, A., Wang, Z.: A quality-of-experience database for adaptive video streaming. IEEE Trans. Broadcast. 64(2), 474–487 (2018). https://doi.org/10.1109/tbc.2018.2822870

  6. Farid, H.: Image forgery detection [a survey]. IEEE Sig. Process. Mag. 26, 2 (2009). https://farid.berkeley.edu/downloads/publications/spm09.pdf. Accessed 27 Dec 2019

  7. Fedak, G., He, H., Moca, M., Bendella, W., Alves, E.: Blockchain-based decentralized cloud computing. Technical report, iExec (2018). https://iex.ec/wp-content/uploads/pdf/iExec-WPv3.0-English.pdf. Accessed 27 Dec 2019

  8. Google: Choose live encoder settings, bitrates, and resolutions (2020). https://support.google.com/youtube/answer/2853702?hl=en

  9. Hammad, R.A.M.: Image quality and forgery detection copula-based algorithms. Ph.D. thesis, Cairo University (2008). https://doi.org/10.24124/2016/bpgub1124

  10. Milani, S., et al.: An overview on video forensics. APSIPA Trans. Sig. Inf. Process. 1, e2 (2012). https://doi.org/10.1017/ATSIP.2012.2

    Article  Google Scholar 

  11. Nielson, S.J., Crosby, S.A., Wallach, D.S.: A taxonomy of rational attacks. In: Castro, M., van Renesse, R. (eds.) IPTPS 2005. LNCS, vol. 3640, pp. 36–46. Springer, Heidelberg (2005). https://doi.org/10.1007/11558989_4

    Chapter  Google Scholar 

  12. Petkanics, D., Tang, E.: Livepeer white paper. Technical report, Livepeer (2018). https://github.com/livepeer/wiki. Accessed 27 Dec 2019

  13. Pinson, M.H.: The consumer digital video library [best of the web]. IEEE Sig. Process. Mag. 30(4), 172–174 (2013). https://doi.org/10.1109/msp.2013.2258265

  14. Rao, K., Yip, P.: Chapter 1 - discrete cosine transform. In: Rao, K., Yip, P. (eds.) Discrete Cosine Transform, pp. 1–6. Academic Press, San Diego (1990). https://doi.org/10.1016/B978-0-08-092534-9.50007-2

  15. Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of the 12th International Conference on Neural Information Processing, NIPS 1999, vol. 12, pp. 582–588 (1999)

    Google Scholar 

  16. Smith, A.R.: Color gamut transform pairs. ACM SIGGRAPH Comput. Graph. 12(3), 12–19 (1978). https://doi.org/10.1145/965139.807361

  17. Teutsch, J., Reitwießner, C.: A scalable verification solution for blockchains. ArXiv abs/1908.04756 (2019)

    Google Scholar 

  18. Timmerer, C., Smole, M., Mueller, C.: Efficient multi-codec support for OTT services: HEVC/h.265 and/or AV1? In: 2018 NAB BEIT Proceedings, p. 5. National Association of Broadcasters (NAB), Washington DC, USA, April 2018

    Google Scholar 

  19. Van der Walt, S., et al.: scikit-image: image processing in python. PeerJ 2, e453 (2014)

    Article  Google Scholar 

  20. Wang, H., et al.: Videoset: a large-scale compressed video quality dataset based on JND measurement. J. Vis. Commun. Image Represent. 46, 292–302 (2017). https://doi.org/10.1016/j.jvcir.2017.04.009, http://www.sciencedirect.com/science/article/pii/S1047320317300950

  21. Wang, Y., Inguva, S., Adsumilli, B.: YouTube UGC dataset for video compression research. In: 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, September 2019. https://doi.org/10.1109/mmsp.2019.8901772

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Authors and Affiliations

  1. Haivision, 2600 Blvd. Alfred-Nobel, 5th Floor, Montreal, QC, H4S 0A9, Canada

    Rabindranath Andujar, Ignacio Peletier, Jesus Oliva & Marc Cymontkowski

  2. Livepeer, 16 Vestry St, Floor 4, New York, NY, 10013, USA

    Yondon Fu, Eric Tang & Josh Allman

Authors
  1. Rabindranath Andujar
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  2. Ignacio Peletier
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  3. Jesus Oliva
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  4. Marc Cymontkowski
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  5. Yondon Fu
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  6. Eric Tang
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  7. Josh Allman
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Corresponding author

Correspondence to Rabindranath Andujar .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

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Andujar, R. et al. (2020). Video Tampering Detection for Decentralized Video Transcoding Networks. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-50347-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50346-8

  • Online ISBN: 978-3-030-50347-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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