Harnessing Motion Blur to Unveil Splicing

Abstract

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.

Camouflaging Motion Blur

“…However, this does not imply that the camouflaging is incorrect since the scope of that method is limited to only space-invariant blur. The output obtained using the more sophisticated method of [18] (that incidentally also employs the projective motion blur model) shows no camouflaging errors ( Fig. 9 (b)) i.e., the camouflaged image is deemed authentic, as expected.…”

“…4(e). As an interesting aside, note that it will not be possible to detect the camouflaged object just from the photometric information using even state-ofthe art splicing detection methods [18] [9] since the motion blur has been perfectly camouflaged. In other words, our compositing is both objective and verifiably correct.…”

Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

“…However, as directly depending on the discrepancy of MBKs of adjacent image patches, it will fail when rotation involves in, as discussed in [7] and [8]. To overcome this issue, [9], [10] estimate nonparameterized MBKs of image patches using [18], model the camera motion using transformation spread function (TSF) based on MBKs, and then identify tamper by comparing the MBKs estimated from the image patches and the deduced MBKs from TSF. The main issues of this method are that, first, the estimation of non-parameterized MBKs highly depends on the content of the image, and is inaccurate on small image patches, as discussed in [18]- [21].…”

“…where (x, y) is the location inK i , M 00 i is the zero order moment, M 10 i and M 01 i are the first order moments, M 20 i , M 02 i and M 11 i are the second order moments. Then the orientationô i ofK i can be obtained by:…”

Digital Image Forgeries and Passive Image Authentication Techniques: A Survey

“…SVM is used for classification and detection rates of 86% on a dataset consisting of 200 images and 83% on 50 images collected from the Internet was achieved. Another detection scheme based on blur as a clue is proposed in [77]. This method expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations.…”