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Distributed Watermarking for Cross-Domain of Semantic Large Image Database

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Book cover Industrial Networks and Intelligent Systems (INISCOM 2020)

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Abstract

This paper proposes a new method of distributed watermarking for large image database that is used for deep learning. We detect the semantic meaning of set of images from the database and embed the a part of watermark into such images set. A part of watermark is one shadow generated from the original watermark by using (n, n) secret sharing scheme. Each shadow is embedded into DCT-SVD domain of one image from the dataset. Since the image sets have multiple image and are distributed in the whole of multiple database, we expect that the proposed method is robust against several attacks.

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Notes

  1. 1.

    http://www.cs.toronto.edu/~kriz/cifar.html.

  2. 2.

    https://datahack.analyticsvidhya.com/contest/practice-problem-identify-the-digits/.

  3. 3.

    http://cocodataset.org/.

  4. 4.

    https://github.com/albanie/shot-detection-benchmarks.

  5. 5.

    https://www.kaggle.com/c/cifar-10.

  6. 6.

    http://trace.eas.asu.edu/yuv/.

  7. 7.

    https://docs.python.org/3/library/random.html.

  8. 8.

    https://jpeg.org/.

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Acknowledgement

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2019.12.

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Authors and Affiliations

  1. Le Quy Don Technical University, 239 Hoang Quoc Viet, Cau Giay, Ha Noi, Vietnam

    Le Danh Tai, Nguyen Kim Thang & Ta Minh Thanh

Authors
  1. Le Danh Tai
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  2. Nguyen Kim Thang
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  3. Ta Minh Thanh
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Corresponding author

Correspondence to Ta Minh Thanh .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, Duy Tan University, Da Nang, Vietnam

    Nguyen-Son Vo

  2. Le Quy Don Technical University, Hanoi, Vietnam

    Van-Phuc Hoang

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Tai, L.D., Thang, N.K., Thanh, T.M. (2020). Distributed Watermarking for Cross-Domain of Semantic Large Image Database. In: Vo, NS., Hoang, VP. (eds) Industrial Networks and Intelligent Systems. INISCOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-030-63083-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-63083-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63082-9

  • Online ISBN: 978-3-030-63083-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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