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Research on image digital watermarking optimization algorithm under virtual reality technology

  • * Corresponding author: Yi Zhang

    * Corresponding author: Yi Zhang 
Abstract Full Text(HTML) Figure(5) / Table(3) Related Papers Cited by
  • Aiming at the shortcomings of current algorithms due to the fixed steps, which is easy to fall into local optimum, with robustness and poor transparency, and cannot be balanced against various common attacks, an optimization algorithm of digital image watermarking algorithm based on Drosophila was proposed. In the support of the virtual reality technology, the original color host image was transformed from the RGB space to YCrCb space, and the pixel block of the Y component was divided into a certain size; according to the principle of forming DC coefficients in the DCT domain, the DC coefficient of each block is calculated directly in the airspace, and the amount of modification for each DC coefficient is determined based on the watermark information and the quantization step size; according to the distribution characteristics of DC coefficient, watermarks are embedded directly in the airspace; the type of digital watermarking and digital watermarking pretreatment methods were determined by using Drosophila optimization algorithm. At the same time, digital watermark embedding, extraction rules and initial steps were selected and identified. The Drosophila optimization algorithm with step size reduces the balance between global and local search ability, which makes up for the shortcomings of traditional algorithm. The experimental results showed that the proposed algorithm can effectively balance the invisibility and robustness of the watermark, and can resist all kinds of common attacks, which with a better visual extraction effect.

    Mathematics Subject Classification: 60J67.


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  • Figure 1.  Image digital watermarking preprocessing under virtual reality technology

    Figure 2.  Color images of the original carrier

    Figure 3.  Original watermark image and scrambled watermark image

    Figure 4.  Watermarked color image

    Figure 5.  Watermarking effects and watermarking extraction effects after different attacks

    Table 1.  PSNR values of the original and watermarked images for each attack

    Attack mode Figure 4 (a) Figure 4 (b)
    No attack 86.4859 87.0142
    Rotation (5 degrees) 72.6435 73.8816
    Image scaling (1/2) 77.5878 75.3243
    JPEG compression (90) 83.6266 84.1118
    Cropping 62.5677 64.2054
     | Show Table
    DownLoad: CSV

    Table 2.  NC value of the original watermark and extracted watermark image under various attacks

    Attack mode Figure 4 (a) Figure 4 (b)
    No attack 1.0000 1.0000
    Salt and pepper noise (0.05) 0.9987 0.9887
    Median filter ($5 \times 5$) 0.9654 0.9574
    Rotation (5 degrees) 0.9613 0.9788
    Image scaling (1/2) 0.9527 0.9755
    JPEG compression (90) 0.9774 0.9802
    Cropping 0.9997 1.0000
     | Show Table
    DownLoad: CSV

    Table 3.  Experimental results of two watermarked images under varying degrees of attack

    Attack mode Vector image Figure 2 (a) NC value Figure 2 (b) NC value
    JPEG compression (quality factor) 90 0.9778 0.9805
    70 0.9594 0.9698
    50 0.9473 0.9587
    30 0.9372 0.9583
    Salt and pepper noise (intensity) 0.05 0.9877 0.9886
    0.10 0.9602 0.9734
    0.15 0.9571 0.9722
    0.20 0.9501 0.9579
     | Show Table
    DownLoad: CSV
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