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Detecting Relevant Regions for Watermark Embedding in Video Sequences Based on Deep Learning

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Intelligent Decision Technologies (IDT 2020)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 193))

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Abstract

In this paper, we propose the original idea for searching the best regions for watermark embedding in the uncompressed and compressed video sequences using a deep neural network. If video sequence is uncompressed, then a huge amount of information can be successfully embedded in the textural regions in each frame or 3D textural volume. The codecs, from MPEG-2 to H.265/HEVC, impose the strict restrictions on a watermarking process due to the standards to transmit any motion in a scene. The basic coding unit is a Group Of Pictures (GOP) including I-frame, P-frame/frames, and B-frame/frames. Among these types of frames, I-frame as a spatial intra-picture prediction from neighboring regions is available for watermarking process. Thus, our goal is to find such frames, which will be I-frames with a high probability, and then detect the textural regions for embedding. The task is complicated by a necessity to detect the scene changes in videos. We use non-end-to-end Siamese LiteFlowNet to detect the frames with low optical flow (non-significant background motion), high optical flow (object motion in a scene), or surveillance failure (scene change).

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References

  1. Favorskaya, M.N., Buryachenko, V.V.: Authentication and copyright protection of videos under transmitting specifications. In: Favorskaya, M.N., Jain, L.C. (eds.) Computer Vision in Advanced Control Systems-5, ISRL, vol. 175, pp. 119–160. Springer, Cham (2020)

    Google Scholar 

  2. Zhu, H., Liu, M., Li, Y.: The Rotation Scale Translation (RST) invariant digital image watermarking using Radon transform and complex moments. Digit. Signal Proc. 20(6), 1612–1628 (2010)

    Article  Google Scholar 

  3. Abdelhakim, A.M., Saleh, H.I., Nassar, A.M.: A quality guaranteed robust image watermarking optimization with artificial bee colony. Expert Syst. Appl. 72, 317–326 (2017)

    Article  Google Scholar 

  4. Kandi, H., Mishra, D., Gorthi, S.R.S.: Exploring the learning capabilities of convolutional neural networks for robust image watermarking. Comput. Secur. 65, 247–268 (2017)

    Article  Google Scholar 

  5. Mun, S.-M., Nam, S.-H., Jang, H., Kim, D., Lee, H.-K.: Finding robust domain from attacks: a learning framework for blind watermarking. Neurocomputing 337, 191–202 (2019)

    Article  Google Scholar 

  6. Favorskaya, M.N., Jain, L.C., Savchina, E.I.: Perceptually tuned watermarking using non-subsampled shearlet transform. In: Favorskaya M.N., Jain L.C. (eds.) Computer Vision in Control Systems-4, ISRL, vol. 136, pp. 41–69. Springer, Cham (2018)

    Google Scholar 

  7. Chen, J., Zhao, G., Salo, M., Rahtu, E., Pietikäinen, M.: Automatic dynamic texture segmentation using local descriptors and optical flow. IEEE Trans. Image Process. 22(1), 326–339 (2013)

    Article  MathSciNet  Google Scholar 

  8. Kaltsa, V., Avgerinakis, K., Briassouli, A., Kompatsiaris, I., Strintzis, M.G.: Dynamic texture recognition and localization in machine vision for outdoor environments. Comput. Ind. 98, 1–13 (2018)

    Article  Google Scholar 

  9. Arashloo, S.R., Amirani, M.C., Noroozi, A.: Dynamic texture representation using a deep multi-scale convolutional network. J. Vis. Commun. Image R. 43, 89–97 (2017)

    Google Scholar 

  10. Tesfaldet, M., Marcus A. Brubaker, M.A., Derpanis, K.G.: Two-stream convolutional networks for dynamic texture synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6703–6712. Salt Lake City, UT, USA (2018)

    Google Scholar 

  11. Tu, Z., Xie, W., Zhang, D., Poppe, R., Veltkamp, R.C., Li, B., Junsong Yuan, J.: A survey of variational and CNN-based optical flow techniques. Sig. Process. Image Commun. 72, 9–24 (2019)

    Article  Google Scholar 

  12. Hui, T.-W., Tang, X., Loy, C.-C.: LiteFlowNet: a lightweight convolutional neural network for optical flow estimation. In: the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8981–8989. Salt Lake City, Utah, USA (2018)

    Google Scholar 

  13. Dosovitskiy, A., Fischer, P., Ilg, E., Höusser, P., Hazırbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: Flownet: learning optical flow with convolutional networks. In: IEEE International Conference on Computer Vision, pp. 2758–2766. Santiago, Chile (2015)

    Google Scholar 

  14. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2462–2470. Honolulu, HI, USA (2017)

    Google Scholar 

  15. Drone Videos DJI Mavic Pro Footage in Switzerland. https://www.kaggle.com/kmader/drone-videos. Last accessed 3 Jan 2020

  16. Favorskaya, M., Pyataeva, A., Popov, A.: Texture analysis in watermarking paradigms. Proc. Comput. Sci. 112, 1460–1469 (2017)

    Article  Google Scholar 

  17. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  18. Teutsch, M., Beyerer, J.: Noise resistant gradient calculation and edge detection using local binary patterns. In: Park, J.I., Kim, J. (eds.) Computer Vision—ACCV 2012 Workshops. LNCS, vol. 7728, pp. 1–14. Springer, Berlin, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image Vis. Comput. 30(2), 86–99 (2012)

    Article  Google Scholar 

  20. Middlebury Dataset. http://vision.middlebury.edu/flow/data/. Last accessed 3 Jan 2020

  21. MPI Sintel Dataset. http://sintel.is.tue.mpg.de/downloads. Last accessed 3 Jan 2020

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Acknowledgments

The reported study was funded by the Russian Fund for Basic Researches according to the research project No. 19-07-00047.

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Authors and Affiliations

  1. Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy ave, Krasnoyarsk, 660037, Russian Federation

    Margarita N. Favorskaya & Vladimir V. Buryachenko

Authors
  1. Margarita N. Favorskaya
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  2. Vladimir V. Buryachenko
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Corresponding author

Correspondence to Margarita N. Favorskaya .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. KES International Research, UK

    Robert J. Howlett

  3. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

    Lakhmi C. Jain

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Favorskaya, M.N., Buryachenko, V.V. (2020). Detecting Relevant Regions for Watermark Embedding in Video Sequences Based on Deep Learning. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. IDT 2020. Smart Innovation, Systems and Technologies, vol 193. Springer, Singapore. https://doi.org/10.1007/978-981-15-5925-9_11

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