基于主密度峰聚类的图像复制–粘贴篡改检测方法
Copy-Move Forgery Detection Based on Main Density Peak Clustering
DOI: 10.12677/aam.2026.153106, PDF,    科研立项经费支持
作者: 龙大强*, 钟域可：广东财经大学统计与数据科学学院，广东 广州；蔺 聪：广东财经大学统计与数据科学学院，广东 广州；信息技术教育部重点实验室(中山大学)，广东 广州
关键词: 复制–粘贴篡改检测；SIFT描述子；分层匹配；主密度峰聚类；Copy-Move Forgery Detection； SIFT Descriptors； Hierarchical Feature Matching； Main Density Peak Clustering
摘要: 随着图像篡改技术的不断发展，复制–粘贴篡改形式日益多样化，给篡改检测与定位带来了新的挑战。针对这一问题，本文提出了一种基于主密度峰聚类的图像复制–粘贴篡改检测方法。该方法以SIFT关键点为基础，通过降低对比度阈值与图像尺度标准化提升关键点提取数量，并采用分层特征匹配策略获取可靠的关键点匹配关系。在此基础上，引入主密度峰聚类算法对匹配关键点进行自适应分组，有效刻画关键点之间的空间结构关系。随后，结合仿射矩阵估计与几何一致性验证，实现对复制–粘贴篡改区域的准确定位。在FAU和MICC-F600数据集上的实验结果表明，本文方法在Precision、Recall和指标上均取得了较为稳定且均衡的检测性能。定量的评价指标与可视化结果验证了该方法在不同分辨率和不同篡改场景下的有效性。
Abstract: With the continuous development of image manipulation techniques, copy-move forgery has become increasingly diverse, posing new challenges to forgery detection and localization. To address this issue, this paper proposes a copy-move image forgery detection method based on main density peak clustering. The proposed method is built upon SIFT keypoints. By reducing the contrast threshold and applying image scale normalization, the number of extracted keypoints is increased, and a hierarchical feature matching strategy is adopted to obtain reliable keypoint correspondences. On this basis, a main density peak clustering algorithm is introduced to adaptively group the matched keypoints, effectively capturing the spatial structural relationships among them. Subsequently, affine transformation estimation combined with geometric consistency verification is employed to accurately localize copy-copy forged regions. Experimental results on the FAU and MICC-F600 datasets demonstrate that the proposed method achieves stable and well-balanced performance in terms of Precision, Recall, and F1. Both quantitative evaluations and visual comparisons verify the effectiveness of the proposed method under different image resolutions and forgery scenarios.
文章引用：龙大强, 蔺聪, 钟域可. 基于主密度峰聚类的图像复制–粘贴篡改检测方法[J]. 应用数学进展, 2026, 15(3): 294-305. https://doi.org/10.12677/aam.2026.153106

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