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A robust SVD-based image watermarking using a multi-objective particle swarm optimization

机译:使用多目标粒子群优化算法的基于SVD的鲁棒图像水印

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The major objective in developing a robust digital watermarking algorithm is to obtain the highest possible robustness without losing the visual imperceptibility. To achieve this objective, we proposed in this paper an optimal image watermarking scheme using multi?objective particle swarm optimization (MOPSO) and singular value decomposition (SVD) in wavelet domain. Having decomposed the original image into ten sub?bands, singular value decomposition is applied to a chosen detail sub?band. Then, the singular values of the chosen sub?band are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. Various combinations of multiple scaling factors are possible, and it is difficult to obtain optimal solutions. Thus, in order to achieve the highest possible robustness and imperceptibility, multi?objective optimization of the multiple scaling factors is necessary. This work employs particle swarm optimization to obtain optimum multiple scaling factors. Experimental results of the proposed approach show both the significant improvement in term of imperceptibility and robustness under various attacks.
机译:开发鲁棒的数字水印算法的主要目的是获得尽可能高的鲁棒性,而又不会失去视觉上的可感知性。为了达到这个目的,本文提出了一种在小波域中使用多目标粒子群算法(MOPSO)和奇异值分解(SVD)的最优图像水印方案。将原始图像分解为十个子带后,将奇异值分解应用于选定的细节子带。然后,通过多个缩放因子(MSF)修改所选子带的奇异值,以嵌入水印图像的奇异值。多个缩放因子的各种组合是可能的,并且难以获得最佳解。因此,为了获得尽可能高的鲁棒性和不可感知性,必须对多个缩放因子进行多目标优化。这项工作采用粒子群优化来获得最佳的多个缩放因子。所提出方法的实验结果表明,在各种攻击下,感知能力和鲁棒性都得到了显着改善。

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