Utilization of edge operators for localization of copy-move image forgery using WLD-HOG features with connected component labeling

One of the most popular image forgery technique is copy-move forgery. In this technique, one or more segments are copied and affixed at different positions within the image. This forgery technique is highly grievous as it can manipulate an image in various ways (such as by presenting additional information or by concealing the genuine information of image). We propose a novel blind forensic technique for copy-move image forgery detection. Our approach utilize different edge detection operators to extract high frequency features. Histogram of Oriented Gradients (HOG) and Weber Local Descriptor (WLD) are used to extract image block features. Radix and lexicographical sorting is enforced over feature vector matrix followed by correlation computation between feature vectors to detect similar feature vectors. Shift vectors are computed to locate similar group of blocks within image. Connected component labeling is applied as morphological operation to remove false matches. Proposed approach is robust to detect plain as well as multiple copy-move forgery in images with post-processing attacks such as contrast adjustment, image blurring, color reduction, and brightness change. Proposed approach achieve highest F-Measure(%) in comparision to other existing forgery detection methods.