信息嵌入位置自适应选择的鲁棒水印算法

为了更好地提高嵌入水印后的图像质量，提出了一种利用遗传算法(GA)优化支持向量回归机(SVR)的鲁棒水印算法。把经Haar小波变换后，图像子带中具有强相似性的数据作为特征向量，用于被遗传算法优化的SVR建立小波系数方向树的模型。通过比较特征向量均方差(MSE)的大小来自适应地确定水印嵌入的位置。水印的嵌入与提取是通过调整模型的预测值与目标值之间的大小来实现的。实验结果表明，所提算法对常见的图像攻击有很强的鲁棒性，而且水印图像在嵌入容量为16384比特的情况下，峰值信噪比可以达到44.15dB。因此能够有效抵抗常见的水印攻击，在嵌入大量信息的情况下，具有很高的透明性。

Robust watermark algorithm for adaptive choice of information embedding position

To improve the quality of watermarked image, an improved robust watermark algorithm based on Support Vector Regression (SVR) and Genetic Algorithm (GA) was proposed. Following Haar wavelet transform, the wavelet coefficients which had strong similarity in image subband were adopted as feature vector, and then the SVR optimized by GA was used to build a wavelet coefficients direction tree model. The values of Mean Square Error (MSE) of the feature vector were compared to adaptively determine the information embedding position. According to the size between the prediction value and real value of the model, the watermark was embedded and extracted. The experimental results show that the proposed algorithm has strong robustness to common image attacks, even the Peak Signal to Noise Ratio (PSNR) can achieve 44.15dB with the embed capacity of 16384 bits. Thus, the proposed algorithm can resist watermarking attacks more effectively and it has high transparency under the situation with big capacity information embedded.