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A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine

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Extreme Learning Machines 2013: Algorithms and Applications

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 16))

Abstract

In this chapter, we present a novel fast and robust watermarking scheme for three different standard video in RGB uncompressed AVI format in DWT domain using a newly developed SLFN commonly known as Extreme Learning Machine (ELM). The embedding is carried out by using scene detection. The LL4 sub-band coefficients of frames constitute the dataset to train the ELM in millisecond time. The output of the ELM is used to embed a binary watermark in the video frames using a pre-specified formula. The resultant video exhibits good visual quality. Five different video processing attacks are executed over signed video. The extracted watermarks from the signed and attacked video yield high normalized correlation (NC) values and low Bit Error Rate (BER) values. This indicates successful watermark recovery and the embedding scheme is found to be robust against these common attacks. It is concluded that the proposed watermarking scheme produces best results due to optimized embedding facilitated by fast training of the ELM. The proposed scheme is found to be suitable for developing real time video watermarking applications due to its low time complexity.

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Author information

Authors and Affiliations

  1. Delhi, 110007, India

    Charu Agarwal

  2. Delhi, 110015, India

    Anurag Mishra

  3. Delhi, 110015, India

    Arpita Sharma

  4. Canberra, Australia

    Girija Chetty

Authors
  1. Charu Agarwal
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  2. Anurag Mishra
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  3. Arpita Sharma
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  4. Girija Chetty
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Corresponding author

Correspondence to Charu Agarwal .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Fuchen Sun

  2. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of (South Korea)

    Kar-Ann Toh

  3. Department of Computer Science and Artificial Intelligence, Universidad Del Pais Vasco, San Sebastian, Spain

    Manuel Grana Romay

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

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Agarwal, C., Mishra, A., Sharma, A., Chetty, G. (2014). A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine. In: Sun, F., Toh, KA., Romay, M., Mao, K. (eds) Extreme Learning Machines 2013: Algorithms and Applications. Adaptation, Learning, and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-04741-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-04741-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04740-9

  • Online ISBN: 978-3-319-04741-6

  • eBook Packages: EngineeringEngineering (R0)

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