圆形特征筛选耦合随机样本的图像真伪决策

摘要

To reduce the complexity of forgery detection algorithm and improve the detection accuracy,an image forgery detection algorithm based on DCT coupled random sample consensus optimization was proposed.The initial image was divided into uniform size image sub blocks and discrete cosine transform (DCT) was used to obtain DCT coefficient of each image block,DCT coefficients were used to characterize the image block.The circular feature selection was constructed,and low frequency DCT coefficient of each image block was extracted,thereby reducing the feature dimension of each block.Dictionary ordering was introduced,and each feature vector was ordered,and the prior threshold was used to match the features.Random sample consensus optimization was introduced to reduce error matching rate of the image block and the image authenticity was decided.Experimental results show that compared with the current image forgery detection algorithm,the proposed algorithm has higher accuracy and efficiency,and lower error matching rate,compared with the current image forgery detection algorithm.%为降低当前图像伪造检测算法的复杂度并提高其检测精度,提出一种圆形特征筛选机制耦合随机样本一致性优化的快速图像真伪决策算法.将检测图像划分为大小一致的子块,进行DCT变换,获得每个图像块的DCT系数,利用DCT系数来表示图像块;构建圆形特征筛选机制,将每个图像块中的DCT低频系数提取出来,降低每个子块的特征维度;引入字典排序机制,对每个特征向量进行排序,利用先验阈值对特征进行匹配;引入随机样本一致性优化策略,对特征匹配进行提纯,降低图像块的误匹配率,对图像真伪完成决策判断.实验结果表明,与当前图像伪造检测算法相比,该算法具有更高的检测精度和更低的误检率,检测效率更高.