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International Symposium on Security in Computing and Communication

SSCC 2016: Security in Computing and Communications pp 305–317Cite as

Extreme Learning Machine for Semi-blind Grayscale Image Watermarking in DWT Domain

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 625))

Abstract

In this paper, an Extreme Learning Machine (ELM) for semi-blind grayscale in DWT domain is proposed. Low frequency LL4 sub-band is used for watermark embedding. ELM is iteratively tuned and used for training and predicting DWT coefficients. The quantized and desired LL4 sub-band coefficients of the DWT domain are used in the input dataset to train the ELM. A random key decides the starting position of the coefficients where the watermark is embedded. Both binary and the random sequence are used as watermark. This process enhances the robustness towards common image processing attacks. Experimental results show that the extracted watermark from watermarked and attacked images are similar to the original watermark. Computed time spans for embedding and extraction are of the order of seconds which is suitable for the real time processing of signed images.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Deen Dayal Upadhyaya College, University of Delhi, Delhi, India

    Ankit Rajpal & Rajni Bala

  2. Department of Electronics, Deen Dayal Upadhyaya College, University of Delhi, Delhi, India

    Anurag Mishra

Authors
  1. Ankit Rajpal
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  2. Anurag Mishra
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  3. Rajni Bala
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Corresponding author

Correspondence to Ankit Rajpal .

Editor information

Editors and Affiliations

  1. IBM ZURICH RESEARCH LABORATORY, IBM ZURICH RESEARCH LABORATORY, RUESCHLIKON, Switzerland

    Peter Mueller

  2. Technology and Management, Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  3. Temple University, New York, New York, USA

    Md Zakirul Alam Bhuiyan

  4. Computer Science, University of Waikato Computer Science, Hamilton, New Zealand

    Ryan Ko

  5. Deakin University , Burwood, Victoria, Australia

    Robin Doss

  6. University of the West of Scotland , Paisley, Glasgow, United Kingdom

    Jose M. Alcaraz Calero

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© 2016 Springer Nature Singapore Pte Ltd.

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Rajpal, A., Mishra, A., Bala, R. (2016). Extreme Learning Machine for Semi-blind Grayscale Image Watermarking in DWT Domain. In: Mueller, P., Thampi, S., Alam Bhuiyan, M., Ko, R., Doss, R., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2016. Communications in Computer and Information Science, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-10-2738-3_26

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  • DOI: https://doi.org/10.1007/978-981-10-2738-3_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2737-6

  • Online ISBN: 978-981-10-2738-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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