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An intermediate significant bit (ISB) watermarking technique using neural networks.

Akram Zeki1, Adamu Abubakar1, Haruna Chiroma2

  • 1Department of Information Systems, International Islamic University Malaysia, P.O. Box 10, 53100 Jalan Gombak, Kuala Lumpur, Malaysia.

Springerplus
|July 8, 2016
PubMed
Summary

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This study introduces a novel watermarking technique using intermediate significant bits (ISB) to improve robustness. A neural network predicts image quality metrics, peak signal to noise ratio (PSNR) and normalized cross-correlation (NCC), after attacks.

Area of Science:

  • Digital Image Processing
  • Information Security
  • Machine Learning

Background:

  • Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC) are traditional metrics for evaluating image watermarking algorithms.
  • Existing methods using PSNR and NCC have limitations in accurately reflecting watermarking algorithm strength and weakness over time.
  • There is a need to establish threshold values for PSNR and NCC to better assess watermarking robustness.

Purpose of the Study:

  • To investigate the threshold values of PSNR and NCC for evaluating watermarking algorithm strength.
  • To develop a novel watermarking technique that enhances robustness using intermediate significant bits (ISB).
  • To build and train a neural network model for predicting PSNR and NCC values after image attacks.

Main Methods:

Keywords:
Bit error rateIntermediate significant bitNormalised cross-correlationPeak signal-to-noise ratio

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  • A novel watermarking technique was employed to embed four watermarks into the ISB of six grayscale images.
  • Image pixels were replaced with minimally altered new pixels to maintain image quality.
  • A neural network was trained using PSNR and NCC values from watermarked images to predict these metrics post-attack.

Main Results:

  • The novel watermarking approach demonstrated improved robustness, as indicated by gathered PSNR and NCC values.
  • The neural network successfully predicted PSNR and NCC values for watermarked images after simulated attacks.
  • Observed that Normalized Cross-Correlation (NCC) values tend to fluctuate before Peak Signal to Noise Ratio (PSNR) values show deterioration.

Conclusions:

  • The proposed ISB watermarking technique offers enhanced robustness compared to traditional methods.
  • The neural network model provides a viable method for predicting watermarking robustness using PSNR and NCC.
  • Understanding the distinct behaviors of PSNR and NCC under attack is crucial for accurate watermarking evaluation.