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    基于四元数指数矩的鲁棒彩色图像水印算法

    Quaternion Exponent Moments Based Robust Color Image Watermarking

    • 摘要: 近年来,抗几何攻击数字图像水印方法研究取得了很大进展,但现有绝大多数图像水印嵌入算法都是针对灰度图像的,直接用于彩色载体图像的数字水印算法较少.即使原始载体是彩色图像,大部分方法也只是通过提取其亮度信息或使用单色通道信息嵌入数字水印.也就说,现有算法未能很好体现和保留不同色彩分量在整个颜色空间内的特定联系,因而必然影响数字水印的鲁棒性和不可感知性.以四元数与指数矩理论为基础,提出了一种基于四元数指数矩的抗几何攻击彩色图像水印算法.1)把传统灰度图像的指数矩理论推广到四元数层面,并定义出彩色图像的四元数指数矩;2)对四元数指数矩的不变特性进行推导与分析;3)构造出基于四元数指数矩的抗几何攻击彩色图像水印方案.仿真实验表明,该算法不仅具有较好的不可感知性,而且对常规信号处理和几何攻击均具有较好的鲁棒性.

       

      Abstract: It is a challenging work to design a robust color image watermarking scheme against geometric distortions. Moments and moment invariants have become a powerful tool in robust image watermarking owing to their image description capability and geometric invariance property. However, the existing moment-based watermarking schemes were mainly designed for gray images but not for color images, and detection quality and robustness will be lowered when watermark is directly embedded into the luminance component or three color channels of color images. Furthermore, the imperceptibility of the embedded watermark is not well guaranteed. Based on algebra of quaternions and exponent moments theory, a new color image watermarking algorithm robust to geometric distortions is proposed in this paper. We firstly introduce the quaternion exponent moments to deal with the color images in a holistic manner, and it is shown that the quaternion exponent moments can be obtained from the conventional exponent moments of each channel. Then we provide a theoretical framework to construct a set of combined invariants with respect to geometric distortions (rotation, scaling, and translation). And finally, we present a new color image watermarking algorithm robust to geometric distortions, which is based on a set of quaternion exponent moments invariants. Experimental results show that the proposed color image watermarking is not only invisible and robust against common signals processing such as median filter, noise adding, and JPEG compression, but also robust against the geometric distortions.

       

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