基于改进显著图和局部特征匹配的copy-move窜改检测

检测整幅窜改图像的方法增加了许多非必要的计算量，为了降低计算复杂度和进一步提高检测精确率，提出了一种基于改进显著图和局部特征匹配的copy-move窜改检测方法。首先，结合图像梯度改进显著图，分离出包含图像高纹理信息的局部显著区域；其次，只对该局部区域采用SIFT(scale invariant feature transform)算法提取特征点；然后，对显著性小的图像采用密度聚类和二阶段匹配策略，对显著性大的图像采用超像素分割和显著块特征匹配的策略；最后，结合PSNR和形态学操作来定位窜改区域。在两个公开数据集上进行实验，该方法的平均检测时间小于10 s,平均检测精确率大于97%,均优于所对比的方法。实验结果表明，该方法能够大幅缩减检测时间、有效提高检测精确率，并且对几何变换和后处理操作也都具有较好的鲁棒性。

Copy-move forgery detection based on improved saliency map and local feature matching

The method of detecting the whole tampered image increases many unnecessary calculations. In order to reduce the computational complexity and further improve the detection accuracy, this paper proposed a copy-move forgery detection method based on improved saliency map and local feature matching. Firstly, it combined the gradient of image to improve the saliency map, and separated the local salient regions containing high texture information of the image. Secondly, it only used SIFT(scale invariant feature transform) algorithm to extract feature points in this local area. Then, it adopted density clustering and two-stage matching strategy for images with low saliency, and adopted the strategy of superpixel segmentation and salient block feature matching for images with high saliency. Finally, it combined PSNR and morphological operations to locate the tampered area. Experiments on two public datasets show that the average detection time of this method is less than 10 s, and the average detection accuracy is greater than 97%, which are better than the compared methods. The experimental results show that this method can greatly reduce the detection time, effectively improve the detection accuracy, and has good robustness to geometric transformation and post-processing operations.