We present a scheme for recovering the orientation of a planar scene from a single translation ally-motion blurred image. By leveraging the homography relationship among image coordinates of 3D points lying on a plane, and by exploiting natural correspondences among the extremities of the blur kernels derived from the motion blurred observation, the proposed method can accurately infer the normal of the planar surface. We validate our approach on synthetic as well as real planar scenes.

The extensive availability of sophisticated image editing tools has rendered it relatively easy to produce fake images. Image splicing is a form of tampering in which an original image is altered by copying a portion from a different source. Because the phenomenon of motion blur is a common occurrence in hand-held cameras, we propose a passive method to automatically detect image splicing using blur as a cue. Specifically, we address the scenario of a static scene in which the cause of blur is due to hand shake. Existing methods for dealing with this problem work only in the presence of uniform space-invariant blur. In contrast, our method can expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations. We validate our method on several examples for different scene situations and camera motions of interest.

Image tampering has become rampant in today's world due to availability of sophisticated image editing tools. In this paper, we deal with the problem of image splicing which is one form of tampering. We propose a passive method to detect the presence of splicing in a given image based on inconsistencies derived from motion blur. Both planar and 3D scenes are considered. The cause of blurring in the image is restricted to translation camera motion while the scene is assumed to be static. We validate our approach on synthetic as well as real examples.