Robust and effective multiple copy-move forgeries detection and localization

Abstract

Copy-move (or copy-paste or cloning) is one of the most common image forgeries, wherein one or more region are copied and pasted within the same image. The motivations of such forgery include hiding an element in the image or emphasizing a particular object. Copy-move image forgery is more challenging to detect than other types, such as splicing and retouching. In recent years, keypoint based copy-move forgery detection, which extracts image keypoints and uses local visual features to identify duplicated regions, exhibits remarkable performance with respect to memory requirement and robustness against various attacks. However, these approaches usually have poor detection ability when copy-move forgeries only involve small or smooth regions. Moreover, they cannot always effectively deal with multiple copy-move forgeries. To tackle these challenges, we propose a robust and effective multiple copy-move forgeries detection and localization method through adaptive keypoint extraction, robust local feature representation, and offsets clustering based post-processing. Firstly, we develop a new image keypoint detector, named generic features from accelerated segment test, and extract adaptively the uniform distribution keypoints from the forged image by employing the adaptive-thresholding and non-maximum suppression. Then, we introduce fast quaternion polar complex exponential transform to describe the image keypoints compactly and distinctively, and utilize the KD tree based K-nearest neighbor matching to find possible correspondences. Finally, the falsely matched pairs are removed by employing the offsets information based candidate clustering, and the duplicated regions are localized using RANSAC and ZNCC algorithm. We conduct extensive experiments to evaluate the performance of the proposed approach, in which encouraging results validate the effectiveness of the proposed technique, especially for plain/multiple copy-move forgeries, in comparison with the state-of-the-art approaches recently proposed in the literature.