Detect image splicing with artificial blurred boundary

In this paper, a novel method is proposed to detect image splicing with artificial blurred boundary based on image edge analysis and blur detection. Different from existing algorithms, the image edges are divided into three types based on the coefficients of the non-subsampled contourlet transform. And, the six-dimensional feature of each edge point is extracted, which is composed of two non-subsampled contourlet coefficients and four statistics based on the phase congruency. Then, three support vector machines for each edge type are trained and used to detect the blurred edge points. And, the local feature is defined to distinguish artificial blurred edge points from defocus ones. The proposed method can be used to detect either the image blur or the image splicing with artificial blurred boundary, and it is shown by experimental results.