匹配对聚类的图像复制粘贴篡改检测  

Image copy-move forgery detection based on the clustering of matched pairs

作　　者：蔺聪 黄轲 温雅敏 卢伟[3] Lin Cong;Huang Ke;Wen Yamin;Lu Wei(Applied Laboratory of Dig Data and Education Statistics,School of Statistics and Mathematics,Guangdong University of Finance and Economics,Guangzhou 510320,China;School of Information,Guangdong University of Finance and Economics,Guangzhou 510320,China;School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou 510006,China)

机构地区：[1]广东财经大学统计与数学学院大数据与教育统计应用实验室,广州510320 [2]广东财经大学信息学院,广州510320 [3]中山大学计算机学院,广州510006

出　　处：《中国图象图形学报》2024年第12期3595-3611,共17页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62072480);广东省普通高校特色创新项目(自然科学)(2022KTSCX041);广州市科技计划基础与应用基础研究项目(202102080316);广东省信息安全技术重点实验室开放基金(2020B1212060078);广州市海珠区科技计划项目(海科工商信计2022-45)。

摘　　要：目的 图像篡改检测主要分为图像区域复制篡改、图像拼接和对象移除3个方向,其中图像复制粘贴篡改是图像篡改检测的重要研究方向之一。针对目前大多数复制粘贴篡改检测方法难以检测平滑和小的篡改区域,且虚警率较高等问题,提出了一种基于匹配对的密度聚类MP-DBSCAN(matched pairs——density based spatial clustering of applications with noise)和点密度过滤策略的图像复制粘贴篡改检测方法。方法 首先,在图像中提取大量关键点,根据关键点的灰度值分组后进行匹配。其次,提出了一种改进的密度聚类算法MP-DBSCAN,聚类对象为匹配对的一侧,并利用匹配对的另一侧约束聚类过程,即使篡改区域在空间上距离较近,或者篡改区域存在多个的情况,也能把不同的篡改区域较好地区分开来。此外,本文还提出了一种点密度过滤策略,通过删除低密度簇,降低了检测结果的虚警率。最后,通过估计仿射矩阵并使用ZNCC (zero-mean normalized cross-correlation)算法定位篡改区域。结果 消融实验表明了MP-DBSCAN算法和点密度过滤策略的有效性。在FAU、MICC-F600、GRIP和CASIA v2.0这4个数据集上与几个经典的和新颖的检测方法进行了对比实验,本文方法的F1在4个数据集上像素层的实验结果分别是0.914 3、0.890 6、0.939 1和0.856 8。结论 本文提出的MP-DBSCAN聚类算法和点密度过滤策略能有效提高检测算法的性能,即使篡改区域经过旋转、缩放、压缩和添加噪声等处理,本文方法依然能够检测出大部分的篡改区域,性能优于当前的检测算法。Objective In recent years,with the development of the internet and computer technology,manipulating images and changing their content have become trivial tasks.Therefore,robust image tampering detection methods need to be developed.As passive forensic methods,image forgery methods can be categorized into copy-move,splicing,and inpainting methods.Copy-move involves copying part of the original image to another part of the same image.Many excellent copymove forgery detection(CMFD) methods have been developed in recent years and can be categorized into block-based,keypoint-based,and deep learning methods.However,these methods have the following drawbacks:1) they cannot easily detect small or smooth tampered regions;2) a massive number of features leads to a high computational cost;and 3) false alarm rates are high when the tampered images involve self-similar regions.To solve these issues,a novel CMFD method based on matched pairs,namely,density-based spatial clustering of applications with noise(MP-DBSCAN),is proposed in this paper along with point density filtering.Method First,a large number of scale-invariant feature transform(SIFT) keypoints are extracted from the input image by lowering the contrast threshold and normalizing the image scale,thus allowing the detection of a sufficient number of keypoints in small and smooth regions.Second,the generalized two nearest neighbor(G2NN) matching strategy is employed to manage multiple keypoint matching,thus allowing the detection algorithm to perform smoothly even when the tampered region has been copied multiple times.A hierarchical matching strategy is then adopted to solve keypoint matching problems involving a massive number of keypoints.To accelerate the matching process,keypoints are initially grouped by their grayscale values,and then the G2NN matching strategy is applied to each group instead of the keypoints detected from the entire image.The efficiency and accuracy of the matching procedure can be improved without deleting the correct matched pairs.Third,an

关 键 词：多媒体取证 图像取证 图像篡改检测 复制粘贴篡改 基于密度的带噪声空间聚类(DBSCAN) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]