基于多支持区域局部亮度序的图像伪造检测  

Image forgery detection based on local intensity order and multi-support region

作　　者：颜普 苏亮亮 邵慧 吴东升 YAN Pu;SU Liangliang;SHAO Hui;WU Dongsheng(Anhui Provincial Key Laboratory of Intelligent Building and Building Energy Conservation(Anhui Jianzhu University),Hefei Anhui 230022,China;College of Electronic and Information Engineering,Anhui Jianzhu University,Hefei Anhui 230601,China)

机构地区：[1]智能建筑与建筑节能安徽省重点实验室(安徽建筑大学),合肥230022 [2]安徽建筑大学电子与信息工程学院,合肥230601

出　　处：《计算机应用》2019年第9期2707-2711,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61672032);安徽省自然科学基金资助项目(1908085QF281);安徽建筑大学博士科研启动基金资助项目(2017QD13,2015QD07)~~

摘　　要：图像伪造检测是目前数字图像处理领域中的一个研究热点,其中复制粘贴是最常用的伪造手段。由于伪造区域在粘贴前会被进行一定的尺度、旋转、JPEG压缩、添加噪声等操作,这使得检测伪造区域具有一定的挑战性。针对图像复制粘贴伪造技术,提出一种基于多支持区域局部亮度序模式(LIOP)的图像伪造检测算法。首先,利用最大稳定极值区域(MSER)算法提取具有仿射不变性的区域作为支持区域;其次,利用非抽样Contourlet变换得到不同尺度、不同分辨率和不同方向的多个支持区域;然后,在每个支持区域上提取同时具有旋转不变性和单调亮度不变性的LIOP描述子,并利用双向距离比值法实现特征初匹配;接着,采用空间聚类将匹配的特征进行归类,进而用随机抽样一致性(RANSAC)算法对每个归类进行几何变换参数估计;最后,使用必要的后处理等操作来检测出伪造区域。实验表明,提出的算法具有较高的伪造检测精度与可信度。Image forgery detection is currently one of the research focuses of digital image processing,and copy-move forgery is the most frequently used techniques in it.The forgery region is subjected to the operations of scale,rotation,JPEG compression,adding noise and so on before the image moved in,thus detecting the forgery becomes hard.Aimming at the image copy-move forgery technology,an image forgery detection algorithm based on Local Intensity Order Pattern(LIOP)and multiple support regions was proposed.Firstly,the affine invariant regions were detected as support regions by Maximally Stable Extremal Region(MSER)algorithm.Secondly,multiple support regions of different scales,resolutions and directions were obtained by NonSubsampled Contourlet Transform(NSCT).Thirdly,the LIOP descriptors invariant to monotonic intensity change and image rotation were extracted on each support region,and the initial feature matching was implemented via bidirectional distance ratio method.Fourthly,spatial clustering was used to classify the matching features,and geometric transformation parameters were estimated for each cluster by using RANdom SAmple Consensus(RANSAC)algorithm.Finally,the essential operations like post-processing were performed to detect the forgery regions.The experimental results show that the proposed algorithm has higher forgery detection accuracy and reliability.

关 键 词：图像伪造 复制粘贴检测 多支持区域 非抽样CONTOURLET变换 局部亮度序模式 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]