基于FAST算子与多特征匹配的图像伪造检测算法

Image Forgery Detection Algorithm Based on FAST Operator and Multi-feature Matching

当前图像伪造检测算法大多采用最近邻与次近邻比值法进行特征匹配来完成图像伪造检测,存在较多的错误检测以及漏检测现象,基于此提出了一种基于FAST算子与多特征匹配的图像伪造检测算法.首先,基于FAST算法与Bresenham方法,构造以像素点为中心的圆形区域,提取图像特征;然后,通过梯度直方图统计法判定特征点的主方向,以特征点为中心建立两级同心圆,并通过求取同心圆在指定方向上的梯度特征,生成特征向量和特征描述子;最后,提取特征点的HSI颜色分量,将HSI颜色分量以及特征点的特征向量作为双重特征,设计了双重特征匹配法则,实现特征匹配.引入Hough变换,对匹配特征点进行聚类,定位伪造内容.实验结果显示,与当前图像匹配算法相比,所提算法具有更高的检测正确度与鲁棒性能.

In order to solve the current image forgery detection algorithms, the nearest neighbor and nearest neighbor ratio method is used to perform image forgery detection, which results in more error detection and leakage detection. An image forgery detection algorithm based on dual feature matching coupled Hough transform clustering has been proposed in this paper. Firstly, the FAST method is used to construct the circular region centered on the pixels, and the image features are extracted by Bresenham method. Secondly, through the gradient histogram statistics method to determine the main direction of the characteristic points, the feature points as the center to construct two concentric circles, and through calculating the concentric gradient in the specified direction, generates a feature vector generation feature descriptor. And Lastly, the HSI color components of feature points are extracted, and the HSI color components and feature vectors are used as the dual features of feature points and then dual feature matching rules are formulated to realize feature matching. The Hough transform is introduced to cluster the matching feature points to locate the fake content. The results show that this algorithm has higher detection accuracy and better robust performance compared with the current image matching algorithm.