Abstract
One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.
Highlights
Image manipulation for the criminal is still a dangerous impact of imaging technology growth
We examine the impact of Gaussian Pyramid Decomposition in copy-move detection with Scale Invariant Feature Transform (SIFT) and Zernike Moments combinations
Due to many false results, there is an imbalance between precision and recall rate
Summary
Image manipulation for the criminal is still a dangerous impact of imaging technology growth. One of the mildest image manipulation techniques is copy-move forgery. This manipulation allows a computer user to hide or add an object with the copied part on the same image (Tyagi, 2018). There are two types of copy-move forgery detection. They are point-based and block-based (Sadeghi, Dadkhah, Jalab, Mazzola, & Uliyan, 2018). Block-based detection is done by sorting and looking for the similarity of the blocks resulting from splitting the input image before determining the damaged area, while the keypoint-based method is the process for generating a vector feature per keypoint which uses to find the similarity of objects in the damaged area
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