An adaptive segmentation based approach for image forgery detection using efficient feature matching

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In the today world digital images are popular sources of information. Digital images can be easily modified using powerful image editing software. The process of creating flexible or emulating documents with the aim of altering the details to earn the profit from the forged image is known as Image forgery. Copy move forgery is the process of creating a forged item by copying a region of the image and pasting it into another region in the same image. An adaptive segmentation based approach for image forgery detection using efficient feature matching the proposed scheme integrates both block-based and key point-based forgery detection methods. Adaptive segmentation devide image into non overlapping blocks and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the Forgery Region Extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions; finally, it applies the morphological operation to the merged regions to generate the detected forgery regions.