Copy Move is a technique widespreadly used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD method is proposed, including double matching process and region localizing process. In double matching process, the first matching is conducted on Delaunay triangles consisting of Local Intensity Order Pattern (LIOP) keypoints, to find the approximate location of suspicious regions. In order to find sufficient keypoint pairs, the existing set of matching triangles is expanded by adding their neighbors iteratively, covering the whole tampered regions, and the second matching with a looser threshold is conducted on the vertices. In the region localizing process, considering the case of multiple copies, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to classify the keypoint pairs described in a new model. Experimental results indicate that the proposed method, with good robustness, outperforms some state-of-the-art methods.

复制移动是一种广泛用于数字图像篡改的技术，这意味着复制移动伪造检测（CMFD）仍是一项重要的研究。本文提出了一种新的CMFD方法，包括双重匹配过程和区域定位过程。在双重匹配过程中，第一次匹配是在由局部强度顺序模式（LIOP）关键点组成的Delaunay三角形上进行的，以找到可疑区域的大概位置。为了找到足够的关键点对，通过迭代地添加它们的邻居来扩展现有的匹配三角形集，以覆盖整个篡改区域，并在顶点上进行具有较宽松阈值的第二次匹配。在区域本地化过程中，考虑到多份副本的情况，基于密度的带噪声应用空间聚类（DBSCAN）用于对新模型中描述的关键点对进行分类。实验结果表明，该方法具有良好的鲁棒性，优于某些最新方法。