Survey On Fraud Image Detection
Image Forgery means manipulation of digital image to conceal meaningful information of the image. The detection of forged image is driven by need of authenticity and to maintain integrity of an image. A copy move forgery detection theme victimization, adaptive over segmentation and has purpose feature matching is used. Earlier block based forgery detection is used but this algorithm has some flaws like: the host image is divided in to overlapping rectangular blocks, which would be computationally expensive, as size of the image increases. The method cannot address the significant geometrical transformations of the forgery regions and their recall rate is low because image blocking method is of regular shape. Although this method can avoid above two problems reducing the computational complexity thus successfully detect the forgery. This method integrates both traditional blocked base forgery detection method and key-point based forgery detection method. We use an image blocking method called as adaptive over segmentation algorithm to divide host image into non-overlapping and irregular blocks adaptively, the feature points are extracted from each image block as block feature instead of being extracted from the whole host image. We use SLIC (simple linear iterative clustering) to segment the host image into meaningful irregular super pixels. To analyze the frequency distribution of the host image DWT (Discrete wavelength transform) is usedto describe the local features in image SIFT (Scale Invariant feature transform) is being used. Another method that we are using for exposing cut-paste based image forgery, contrast enhancement detection technique is adapted. To detect contrast enhancement in an image histogram based detection is applied.
SLIC, DWT, SIFT, Histogram, Equalization.