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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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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 .

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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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