Detection of copy-move forgery in digital images using Singular Value Decomposition

Abstract

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition (SVD). It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size. At each step, the SVD is determined. First, the diagonal matrix’s maximum value (norm) is selected (representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating the matrix or scaled). Then, the similar norms are grouped, and each leading group is separated into many subgroups (elements of each subgroup are neighbors) according to 8-adjacency (the subgroups for each leading group must be far from others by a specific distance). After that, a weight is assigned for each subgroup to classify the image as forgery or not. Finally, the F1 score of the proposed system is measured, reaching 99.1%. This approach is robust against rotation, scaling, noisy images, and illumination variation. It is compared with other similar methods and presents very promised results.