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A robust approach of watermarking in contourlet domain based on probabilistic neural network

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Abstract

A novel algorithm of image watermarking in contourlet transform(CT) has been proposed based on probabilistic neural network(PNN) in this paper. In the proposed process, host image is divided into several blocks, and then each block will be decomposed by CT firstly. Then coefficients of each block will be divided into many small coefficient blocks, and the Pseudo random Noise(PN) sequence is generated for the watermarking and embedded into the coefficient blocks that we selected with certain intensity. Thirdly, the correlation between each of embedded coefficient block and same-sized PN sequence is calculated, and put as the input of the probabilistic neural network system for training. Fastly, after training, the trained system will be robust for watermarking extraction. Results show that this scheme is blind, strongly robust and perceptual invisible, which are two major characteristics for digital watermarking, and does not need the watermark in the extraction process.

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Acknowledgements

The authors would like to thank anonymous reviewers for their detailed comments and questions which improved the quality of the presentation of this paper. This research is supported in part by the National Natural Science Foundation of China (grant No. 61472278).

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Authors and Affiliations

  1. Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, binshuixidao no 391, Tianjin, 300384, China

    Jia-Xing Liu, Xian-bin Wen, Li-Ming Yuan & Hai-Xia Xu

  2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Jia-Xing Liu, Xian-bin Wen, Li-Ming Yuan & Hai-Xia Xu

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  1. Jia-Xing Liu
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  2. Xian-bin Wen
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  4. Hai-Xia Xu
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Correspondence to Jia-Xing Liu.

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Liu, JX., Wen, Xb., Yuan, LM. et al. A robust approach of watermarking in contourlet domain based on probabilistic neural network. Multimed Tools Appl 76, 24009–24026 (2017). https://doi.org/10.1007/s11042-016-4178-4

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  • DOI: https://doi.org/10.1007/s11042-016-4178-4

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