An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal

• Published in: Journal of King Saud University. Computer and information sciences Vol. 33; no. 9; pp. 1055 - 1063
• Main Authors: Hegazi, Aya, Taha, Ahmed, Selim, Mazen M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2021; Elsevier
• Subjects: Copy-move detection; DBSCAN; GORE; Image forensics; Keypoint-based methods; Multiple-copied matching; Copy-move detection; Multiple-copied matching; Image forensics; DBSCAN; GORE; Keypoint-based methods
• ISSN: 1319-1578; 2213-1248; 2213-1248
• DOI: 10.1016/j.jksuci.2019.07.007

Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint- based methods suffer from high complexity. In this paper, an improved approach for keypoint- based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple cloning.