A Novel Fusion Algorithm for Copy-Move Forgery Detection

An algorithm that fused block-based algorithm and keypoints-based algorithm is proposed for the copy-move forgery detection. Firstly, the popular keypoint-based algorithms, such as SURF, the nearest-neighbor (2NN) test, adaptive Euclidean distance and Random sample consensus (RANSAC) are applied to extract, match and filter out most of mismatched feature points and get the candidate inlier matches. The RANSAC are addressed to classify the candidate inlier matches. Then, Radial Harmonic Fourier Moments is proposed to extract invariances of the candidate inlier matches in circle blocks. Finally, the host image segment into texture patches. A series of the experiments showed that the proposed fusion algorithm can achieve superior performances than the moment invariant algorithms under various geometric transformations.