Image tampering detection by exposing blur type inconsistency
In this paper, we propose a novel method for image tampering detection in multi-type blurred images. After block-based image partitioning, a space-variant prior for local blur kernels is proposed for local blur kernels estimation. Then, the image blocks are clustered using a k-means clustering based on the similarity of local blur kernels to generate blur type invariant regions. Finally, blur types of the regions are classified into out-of-focus or motion blur using a minimum distance classifier. The experimental results show that the proposed method successfully detects and classifies the regions blur types which outperforms the state-of-the-art techniques. Our proposed approach is used to detect inconsistency in the partial blur types of an image as an evidence of image tampering.