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

  • Ankit RajpalEmail author
  • Anurag Mishra
  • Rajni Bala
Conference paper
Part of the Communications in Computer and Information Science book series (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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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceDeen Dayal Upadhyaya College, University of DelhiDelhiIndia
  2. 2.Department of ElectronicsDeen Dayal Upadhyaya College, University of DelhiDelhiIndia

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