中文摘要：

目的为协调水印算法不可见性与鲁棒性之间的矛盾，提高水印算法抵抗几何攻击的能力，提出一种图像块的不可见性与鲁棒性均衡水印算法。方法将宿主图像分成互不重叠的图像块，利用人类视觉系统的掩蔽特性对每个图像块的纹理特征和边缘特征进行分析，选择掩蔽性好的图像块作为嵌入子块。对嵌入子块作2级离散小波变换，将其低频子带进行奇异值分解，通过修改U矩阵第1列元素间的大小关系嵌入Arnold置乱后的水印信息。在水印提取前，对几何失真含水印图像利用图像尺度不变特征变换（SIFT）特征点的坐标关系和尺度特征进行几何校正，恢复水印的同步性。结果对标准灰度图像进行实验，含水印图像的峰值信噪比都可以达到44dB以上。对含水印图像进行常规攻击和几何攻击，提取出的水印图像与原始水印图像的归一化互相关系数大部分都能达到0．99以上，说明该算法不仅具有良好的不可见性，对常见攻击和几何攻击都具有较强的鲁棒性。结论选择掩蔽性好的图像块作为水印嵌入位置能够充分保证水印算法的不可见性，特别是水印提取前利用SIFT特征点具有旋转、缩放和平移不变性对几何失真含水印图像实现有效校正，提高了含水印图像抵抗几何攻击的能力，较好地协调水印算法不可见性与鲁棒性之间的矛盾。

英文摘要：

Objective Embedding watermark information into the host image leads to a contradiction between invisibility and robustness. High watermark embedding strength means strong watermark robustness but poor invisibility. Low watermark embedding strength means good watermark invisibility but weak robustness. As an effective means of copyright protection, a watermarking algorithm must ensure good invisibility and effectively resist various attacks. Geometric attacks destroy the synchronization between the watermark and host image and thus leads to the failure of the watermarking algorithm. To ad dress the contradiction between invisibility and algorithm robustness and improve the capability to resist geometric attacks, this study proposes an invisible and robust watermarking algorithm based on an image block. Method The host image is divided into nonoverlapping image blocks, and the texture and edge features of each image block are analyzed by using the masking property of the human visual system to calculate the masking value of each image block. The masking values are arranged in a descending order, and an appropriate number of good masking image blocks are selected as embedded subblocks according to the size of the watermark information. Twolevel discrete wavelet transform is performed on the sub block, and its lowfrequency subband is decomposed by singular value decomposition to obtain orthogonal matrices U and V and diagonal matrix S. The difference among the three sets of elements in the first column of the U orthogonal matrix is calculated according to the watermark bit information. If the difference is less than the threshold value, the Arnold scram bled watermark information is embedded into the U orthogonal matrix. Then, inverse singular value decomposition is ap plied on the selected image block, and the lowfrequency subband and other middle and highfrequency subbands of the image block are subjected to inverse wavelet transform. Afterward, all the image blocks are combined to obtain watermarked images. The sc