摘要
针对现有图像复制粘贴篡改检测算法计算复杂度过高的问题,提出了一种基于分组尺度不变特征变换的图像复制粘贴篡改快速检测算法。首先,利用简单线性迭代聚类将输入图像分割成非重叠且不规则的块;然后,根据图像块内结构张量属性将其分为平坦块、边缘块和角点块,提取图像块内的SIFT特征点作为块特征;最后,通过块特征的类间匹配定位篡改区域。所提算法通过图像块分类和类间匹配,在保证检测效果的同时,有效地降低了特征匹配定位篡改区域阶段的时间复杂度。实验结果表明,所提算法检测准确率为97.79%,召回率为90.34%,F值为93.59%;图像尺寸为1024像素×768像素时算法时间复杂度为12.72 s,图像尺寸为3000像素×2000像素时算法时间复杂度为639.93 s。与已有的复制粘贴算法相比,所提算法能够快速精准地定位篡改区域,且具有较好的稳健性。
Aiming at the high computational complexity of the existing copy-move image forgery detection algorithm,a copy-move forgery detection algorithm based on group scale-invariant feature transform(SIFT)was proposed.Firstly,the simple linear iterative clustering(SLIC)was utilized to divide the input image into non-overlapping and irregular blocks.Secondly,the structure tensor was introduced to classify each block as flat blocks,edge blocks and corner blocks,and then the SIFT feature points extracted from the block were taken as the block features.Finally,the forgery was located by the inter-class matching of the block features.By means of inter-class matching and feature point matching,the time complexity of the proposed copy-move forgery detection algorithm in feature matching and locating forgery region was effectively reduced while guaranteeing the detection effect.The experimental results show that the accuracy of the proposed algorithm is 97.79%,the recall rate is 90.34%,and the F score is 93.59%,the detecting time for the image with size of 1024×768 is 12.72 s,and the detecting time for the image with size of 3000×2000 was 639.93 s.Compared with the existing copy-move algorithm,the proposed algorithm can locate the forgery region quickly and accurately,and has high robustness.
作者
肖斌
景如霞
毕秀丽
马建峰
XIAO Bin;JING Ruxia;BI Xiuli;MA Jianfeng(Key Laboratory of Image Cognition,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;School of Cyber Engineering,Xidian University,Xi’an 710071,China)
出处
《通信学报》
EI
CSCD
北大核心
2020年第3期62-70,共9页
Journal on Communications
基金
国家重点研发计划基金资助项目(No.2016YFC1000307-3)
国家自然科学基金资助项目(No.61976031,No.61806032)
重庆市基础与前沿基金资助项目(No.cstc2018jcyjAX0117)
重庆市教委科学技术研究计划重点基金资助项目(No.KJZD-K201800601)。
