中文摘要：

提出一种基于支持向量机的自适应图像空域水印嵌入算法.由于支持向量机与人眼视觉系统在自学习、泛化和非线性逼近等方面具有极大的相似性,算法利用支持向量机模拟人眼视觉特征,结合图像的局部相关特性,自适应地确定图像的最佳嵌入位置和嵌入强度.首先,利用无导师的模糊聚类分析方法对图像各像素进行初步的聚类,为有导师的支持向量机找到分类规则;然后,从各类别中选出隶属度超过一定阈值的像素作为支持向量机分类的训练样本集,建立支持向量机的分类模型,根据此模型对图像各像素再次分类,从而确定水印的最佳嵌入位置;最后结合图像自身的局部相关性,自适应地调整水印嵌入位置的像素值.该算法在提取水印时不需要原始载体图像.实验结果表明,此算法对多种图像处理均具有很好的稳健性和图像感知质量,其性能优于相关文献上的相近方法.

英文摘要：

An adaptive spatial domain image watermarking algorithm based on support vector machine （SVM） is proposed. Since there is very close similarity between SVM and human visual system （HVS） in self-learning, generalization and non-linear approximation, the watermark embedding locations and strength can be adaptively identified by applying SVM algorithm based on the HVS. In this scheme, a kind of unsupervisory machine learning method, named fuzzy c-mean clustering algorithm, is first used to label the pixels in a cover image. Then, only those pixels whose subiection-value exceed a given threshold are selected from each label to be the training sample set of SVM. Sequentially, an SVM based multi- classification model is established. According to this model, the watermark embedding locations are further optimized. Finally, a bit of the watermark is adaptively embedded by adjusting the corresponding pixel value, according to the image local correlation. The presented watermarking scheme can extract the watermark without the help of the original image. Experimental results show that the proposed adaptive scheme has both sound perceptual quality and high robustness to various signal processing such as lossy compression, noise addition, image enhancement, filtering, cropping, mosaic, blurring, and so on. The watermarking performance notably outperforms the similar algorithm.