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    An adaptive digital image watermarking scheme with PSO, DWT and XFCM

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    Date
    2017-02
    Publisher
    © 2017 IEEE
    Author
    Mitashe, Mashruha Raquib
    Habib, Ahnaf Rafid Bin
    Razzaque, Anindita
    Tanima, Ismat Ara
    Uddin, Jia
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/9498
    Citation
    Mitashe, M. R., Habib, A. R. B., Razzaque, A., Tanima, I. A., & Uddin, J. (2017). An adaptive digital image watermarking scheme with PSO, DWT and XFCM. Paper presented at the 2017 IEEE International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2017, 10.1109/ICIVPR.2017.7890868
    Abstract
    In this paper, a novel adaptive digital image watermarking model based on modified Fuzzy C-means clustering is proposed. For watermark embedding process, we used Discrete Wavelet Transform (DWT). A segmentation technique XieBeni integrated Fuzzy C-means clustering (XFCM) is used to identify the segments of original image to expose suitable locations for embedding watermark. We also pre-processed the host image using Particle Swarm Optimization (PSO) to lend a hand to the clustering process. The goal is to focus on proper segmentation of the image so that the embedded watermark can withstand common image processing attacks and provide security to digital images. Several attacks were performed on the watermarked images and original watermark was extracted. Performance measures like PSNR, MSE, CC were computed to test the extracted watermarks with and without attacks. Experimental results show that the proposed scheme has performed well in terms of imperceptibility and robustness when compared to other watermarking models.
    Keywords
    Digital watermarking; DWT; FCM; PSO; XieBeni Index
     
    Description
    This article was published in the IEEE Xplore [© 2017 IEEE] and the definite version is available at : http://doi.org/10.1109/ICIVPR.2017.7890868 The Journal's website is at: http://ieeexplore.ieee.org/document/7890868/?reload=true
    Publisher Link
    http://ieeexplore.ieee.org/document/7890868/?reload=true
    DOI
    http://doi.org/10.1109/ICIVPR.2017.7890868
    Department
    Department of Computer Science and Engineering, BRAC University
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