基于果蝇优化算法的小波域数字水印算法

为了平衡水印的透明性和鲁棒性,提出了基于果蝇优化算法(FOA)的小波域数字水印算法。该算法利用果蝇优化算法将离散小波变换(DWT)应用到水印技术中,通过群体智能算法解决水印的透明性和鲁棒性之间的矛盾。为了保护数字图像的版权信息,将所选择的原始图像通过二维离散小波变换分解,然后将经过Arnold变换后的水印图像较优地嵌入到小波的垂直子带系数中,这样可以保证图像的质量。在优化过程中,缩放因子是通过FOA不断地被训练和更新的。此外,还提出一个新的算法框架,通过DWT域预测可行性来评估参数。实验结果表明,所提算法具有较高的透明性和鲁棒性,水印相似度在0.95以上,与现有的一些基于群智能算法的水印方法相比,在对抗旋转和剪切等几何攻击提高了10%。

Wavelet domain digital watermarking method based on fruit fly optimization algorithm

For balancing transparency and robustness of watermark, this paper proposed wavelet-domain digital watermarking method based on Fruit Fly Optimization Algorithm (FOA). The algorithm used Discrete Wavelet Transform (DWT) by FOA to watermarking technology and solved the contradiction between transparency and robustness in the watermark by swarm intelligence algorithm. In order to protect the copyright information of digital image, the selected original image was decomposed through a two-dimensional discrete wavelet transform, and watermark image through Arnold transformation was better embedded into wavelet coefficients of vertical sub-band, which guaranteed image quality. In the optimization process, the scaling factor was continuously being trained and updated by FOA. In addition, a new algorithm framework was proposed, which evaluated the scaling factor by prediction feasibility of DWT domain. The experimental results show that, the proposed algorithm has higher transparency and robustness against attacks, with watermarking similarity above 0.95, and 10% higher under geometric attacks such as rotation and shearing compared to some existing watermarking methods based on swarm intelligence.