A new keypoint-based copy-move forgery detection for color image

Over the past decade, many efforts have been made in copy-move forgery detection (CMFD), and some promising methodologies have been proposed to detect copy-move forgeries. Keypoint based CMFD approaches extract image interest points and use local visual features to identify duplicated regions, which exhibit remarkable performance with respect to memory requirement and computational cost. But unfortunately, they usually use the pure gray-based detectors to extract interest points in which the significant color information is ignored. Also, local visual features are computed without considering the correlation between different color channels. All this lower inevitably the detection and localization accuracy for color tampered image. In this paper we propose a new technique for the detection and localization of copy-move forgeries, which is based on color invariance model and quaternion polar complex exponential transform (QPCET). First, stable color image interest points are extracted by using new interest point detector, in which the SURF (speeded up robust features) detector and color invariance model are incorporated. Then, a set of connected Delaunay triangles is built based on the extracted color image interest points, and suitable local visual features of the triangle mesh are computed using QPCET. Afterwards, local visual features are employed to match triangular meshes by a combination of reversed-generalized 2 nearest-neighbor (Rg2NN) and best bin first (BBF). Finally, the falsely matched triangular meshes are removed by customizing the random sample consensus, and the duplicated regions are localized using zero mean normalized cross-correlation measure. Compared with the state-of-the-art approaches, extensive experimental results prove that our proposed method can detect and localize color image copy-moves with good accuracy even in adverse conditions.