Manoj Kumar Pandey

Deep artificial neural network based blind color image watermarking

• Authors Details :  
• Sushma Jaiswal,  
• Manoj Kumar Pandey

Journal title : Springer Tracts in Human-Centered Computing

Publisher : Springer Nature Singapore

Online ISSN : 2662-6934

Page Number : 101-112

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Digital data is growing enormously as the year passes and therefore there is a need of mechanism to protect the digital contents. Image watermarking is one of the important tools for the human to provide copyright protection and authorship. For achieving the ideal balance between imperceptibility and robustness, a robust blind color image watermarking employing deep artificial neural networks (DANN), LWT and the YIQ color model has been presented. In the suggested watermarking method, an original 512-bit watermark is applied for testing and a randomly generated watermark of the same length is used for training. PCA is used to extract 10 statistical features with significant values out of 18 statistical features, and binary classification is used to extract watermarks here. For the four images Lena, Peppers, Mandril, and Jet, it displays an average imperceptibility of 52.48 dB. For the threshold value of 0.3, it does an excellent job of achieving good balance between robustness and imperceptibility. Except for the gaussian noise, rotation, and average filtering attacks, it also demonstrates good robustness against common image attacks. The results of the experiment demonstrate that the suggested watermarking method outperforms competing methods.

Article DOI & Crossmark Data

DOI : https://doi.org/10.1007/978-981-99-3478-2_10

Article Subject Details

• Computational Mathematics
• Computer Science
• Computer Science Applications

Article Keywords Details

• deep artificial neural network
• YIQ
• Color image
• Image watermarking
• Robust watermarking

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Article References

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