结合结构与梯度的图像哈希算法

为了提高分类性能和运算效率，提出结合结构特征与梯度特征的图像哈希算法. 该算法对输入图像进行预处理提高算法的鲁棒性，将预处理后的图像转换到YCbCr颜色空间，提取亮度Y分量. 利用Y分量的峰顶曲线和峰谷曲线来获取外部结构特征，同时提取峰顶和峰谷的位置信息来构建内部结构特征. 结合外部结构特征和内部结构特征得到图像的结构特征；提取Y分量的横向梯度与纵向梯度来构建图像的梯度特征；将结构特征与梯度特征联合起来并扰乱得到最终的哈希序列. 实验结果表明，所提算法对亮度调整、对比度调整和高斯低通滤波等保持内容的图像处理较稳健. 与已有算法对比，该算法具有更好的受试者工作特性（ROC）曲线和较好的图像分类性能，在篡改检测实验中，该算法可以有效地检测篡改图像.

Image Hashing algorithm based on structure and gradient

An image Hashing algorithm based on structure features and gradient features was proposed to improve the classification performance and efficiency of Hashing algorithm. The input image is pre-processed to improve the robustness of the algorithm, and then the pre-processed image is transformed into YCbCr color space for extracting the brightness Y component. The external structure feature is obtained by using the peak and valley curves of Y component, and the internal structure feature is obtained by extracting the position information of the peak and valley. The external and internal structure features are combined to produce structure features of the image. The horizontal and vertical gradients of Y component are extracted to construct the gradient features. The final Hash is produced by combining and disturbing the structure features and gradient features. Experimental results show that the proposed algorithm is robust to some common content-preserving image processing such as brightness adjustment, contrast adjustment and Gaussian low-pass filtering. The proposed algorithm has better receiver operating characteristic（ROC） curve and better image classification performanc, compared with the existing Hashing algorithms. The tampering detection experiment shows that the algorithm can effectively detect tampered images.