基于张量分解和联合熵的双彩色鲁棒水印算法

Dual-color Robust Watermarking Algorithm Based on Tensor Decomposition and Joint Entropy

针对彩色图像水印算法在滤波攻击下鲁棒性较差的问题,提出一种基于张量分解和联合熵的双彩色鲁棒水印算法.首先,将彩色载体图像整体进行张量分解得到其张量特征图并分块;然后,利用联合熵提取遮蔽性较好的特征图子块;最后,将水印信息嵌入到选定块经过奇异值分解(singular value decomposition,SVD)后的U矩阵中.实验结果表明,Lena图像峰值信噪比达39 dB以上,该算法不仅能有效抵抗图像的滤波攻击,NC值最高达1.0000,而且对其他常规图像攻击以及几何攻击也有较强的鲁棒性.

Aiming at the problem of poor robustness of color image watermarking algorithms under filtering attacks,we proposed a dual-color robust watermarking algorithm based on tensor decomposition and joint entropy.Firstly,the color carrier image was tensor-decomposed as a whole to obtain its tensor feature map and divided into blocks.Secondly,joint entropy was used to extract the feature map sub-blocks with better shadowing.Finally,the watermark information was embedded into the[WTHX]U[WT]matrix of the selected block after singular value decomposition(SVD).The experimental results show that the peak signal-to-noise ratio of Lena images is more than 39 dB.The algorithm can not only effectively resist image filtering attacks,with a maximum NC value of 1.0000,but also has strong robustness against other conventional image attacks and geometric attacks.