In a common type of digital image forgery, called copy–move forgery, a part of the image is copied and pasted into the another part of the same image, typically with the intention to hide an object or a region.

In this work we focused on detecting a common type of digital image forgery, called copy-move forgery. In copy-move forgery, a part of the image is copied and pasted into another part of the same image, with the intention to hide an object or a region of the image. Duplicated regions may not always match exactly. This could be caused by a lossy compression algorithm, such as JPEG, or by possible use of the retouch tool. Our method is based on blur moment invariants, which allows successful detection of copy-move forgery, even when blur degradation, additional noise, or arbitrary contrast changes are present in the duplicated regions.

The main steps of the method are:

• tiling the image with overlapping blocks,

• blur moment invariants representation of the overlapping blocks,

• principal component transformation,

• k-d tree representation,

• blocks similarity analysis,

• near-duplicated regions map creation

An example of the method's output is shown in the image below. Shown are the original version of the test image (top-left), its forged version (top-right), the modified region (bottom-left) and the constructed duplication map (bottom-right).

Results obtained show that the use of blur moment invariants can improve the detection abilities of the copy-move forgery detection methods.

Contact person: Babak Mahdian