基于分组SIFT的图像复制粘贴篡改快速检测算法

doi:10.11959/j.issn.1000-436x.2020045

通信学报 ›› 2020, Vol. 41 ›› Issue (3): 62-70.doi: 10.11959/j.issn.1000-436x.2020045

基于分组SIFT的图像复制粘贴篡改快速检测算法

肖斌¹,景如霞¹,毕秀丽¹(),马建峰²

¹ 重庆邮电大学图像认知重点实验室，重庆 400065
² 西安电子科技大学网络与信息安全学院，陕西西安 710071

修回日期:2020-02-05 出版日期:2020-03-25 发布日期:2020-03-31
作者简介:肖斌（1982- ），男，重庆人，博士，重庆邮电大学教授、博士生导师，主要研究方向为图像处理、模式识别等|景如霞（1994- ），女，四川绵阳人，重庆邮电大学硕士生，主要研究方向为图像处理、模式识别|毕秀丽（1982- ），女，黑龙江牡丹江人，博士，重庆邮电大学副教授、硕士生导师，主要研究方向为数字图像处理、多媒体信息安全等|马建峰（1963- ），男，陕西西安人，博士，西安电子科技大学教授、博士生导师，主要研究方向为网络信息安全、模式识别
基金资助:
国家重点研发计划基金资助项目(2016YFC1000307-3);国家自然科学基金资助项目(61976031);国家自然科学基金资助项目(61806032);重庆市基础与前沿基金资助项目(cstc2018jcyjAX0117);重庆市教委科学技术研究计划重点基金资助项目(KJZD-K201800601)

Fast copy-move forgery detection algorithm based on group SIFT

Bin XIAO¹,Ruxia JING¹,Xiuli BI¹(),Jianfeng MA²

¹ Key Laboratory of Image Cognition,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
² School of Cyber Engineering,Xidian University,Xi’an 710071,China

Revised:2020-02-05 Online:2020-03-25 Published:2020-03-31
Supported by:
The National Key Research and Development Program of China(2016YFC1000307-3);The Chongqing Research Program of Application Foundation ＆Advanced Technology(cstc2018jcyjAX0117);The Scientific ＆ Technological Key Research Program of Chongqing Municipal Education Commission(KJZD-K201800601)

摘要/Abstract

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1 024像素×768像素时算法时间复杂度为12.72 s，图像尺寸为3 000像素×2 000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。

关键词: 复制粘贴篡改检测, 特征匹配, SIFT, 结构张量

Abstract:

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.

Key words: copy-move forgery detection, feature matching, SIFT, structure tensor

中图分类号:

TP391

肖斌,景如霞,毕秀丽,马建峰. 基于分组SIFT的图像复制粘贴篡改快速检测算法[J]. 通信学报, 2020, 41(3): 62-70.

Bin XIAO,Ruxia JING,Xiuli BI,Jianfeng MA. Fast copy-move forgery detection algorithm based on group SIFT[J]. Journal on Communications, 2020, 41(3): 62-70.

图/表 11

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参考文献 32

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特征提取方法	匹配方法
DCT	穷举搜索
SVD	穷举搜索
PCA和DWT	穷举搜索
HOG	穷举搜索
Krawtchouk矩阵	穷举搜索
Zernike矩阵	近似搜索
极性复数指数变换矩阵和YCbCr颜色空间	穷举搜索
极性复数变换矩阵	近似搜索
一维描述符	穷举搜索
一致性敏感哈希	近似搜索
空间和颜色模型	近似搜索

特征提取方法	匹配方法
SURF	穷举搜索
MROGH	穷举搜索
BRISK	穷举搜索
双阈值SIFT描述符	穷举搜索
Harris角点	穷举搜索
SIFT描述符和Zernike矩阵	穷举搜索
SIFT	穷举搜索

特征提取方法	匹配方法
adaptive superpixel + SIFT descriptor	穷举搜索
SIFT descriptor	穷举搜索
polar cosine transform	近似搜索
multi-scale superpixel + SIFT descriptor	近似搜索

类型	参数	范围	步长	数量/幅
原始图像	—	—	—	36
篡改图像	—	—	—	36
篡改+旋转攻击	角度	2°～10°	2°	180
篡改+尺度缩放攻击	缩放因子	91%～109%	2%	360

ε₂	Precision	Recall	F	Time_1/s	Time_2/s
0.1	0.612 7%	0.545 4%	0.577 0%	24.164 2	2 077.559 5
0.2	0.851 3%	0.701 3%	0.769 0%	23.738 2	1 615.536 8
0.3	0.970 9%	0.903 4%	0.935 9%	12.722 9	639.933 5
0.4	0.904 7%	0.855 4%	0.879 3%	25.252 2	2 270.522 5
0.5	0.924 9%	0.843 6%	0.882 3%	28.795 1	2 573.456 5
0.6	0.940 5%	0.895 8%	0.912 9%	11.957 6	1 038.746 2
0.7	0.940 6%	0.890 4%	0.909 8%	15.112 8	1 207.580 1
0.8	0.940 6%	0.890 4%	0.909 8%	14.897 7	1 203.711 3
0.9	0.940 6%	0.890 4%	0.909 8%	17.699 3	1 489.668 1
1.0	0.940 6%	0.890 4%	0.909 8%	16.965 2	1 406.540 9

基于分组SIFT的图像复制粘贴篡改快速检测算法

Fast copy-move forgery detection algorithm based on group SIFT

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摘要/Abstract

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图/表 11

参考文献 32

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Metrics

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ε₁	Precision	Recall	F	Time_1/s	Time_2/s
0.1	0.921 5%	0.848 0%	0.878 1%	13.037 5	905.064 2
0.2	0.921 5%	0.848 0%	0.878 1%	13.042 6	905.475 5
0.3	0.921 5%	0.848 0%	0.878 1%	16.298 0	1 149.179 6
0.4	0.921 4%	0.853 5%	0.881 1%	15.882 6	1 147.048 5
0.5	0.876 0%	0.764 7%	0.792 0%	11.144 2	871.759 6
0.6	0.826 2%	0.755 7%	0.789 3%	12.893 5	949.070 0
0.7	0.892 7%	0.804 4%	0.846 2%	12.879 9	890.019 4
0.8	0.892 3%	0.805 7%	0.846 9%	12.679 8	901.484 0
0.9	0.970 9%	0.903 4%	0.935 9%	12.722 9	639.933 5
1.0	0.952 6%	0.880 8%	0.911 9%	12.238 1	1 544.191 9

算法	Precision	Recall	F	Time_1/s	Time_2/s
文献[11]算法	0.840 0%	0.994 3%	0.902 1%	6.624 0	74.170 0
文献[6]算法	0.959 5%	0.820 4%	0.899 7%	49.538 1	969.070 0
文献[10]算法	0.969 8%	0.760 8%	0.861 2%	121.085 6	1787.381 5
文献[22]算法	0.918 4%	0.752 6%	0.806 8%	22.377 9	986.328 7
文献[21]算法	0.946 8%	0.967 1%	0.953 8%	17.125 6	108.795 2
文献[12]算法	0.649 0%	0.164 7%	0.312 4%	108.922 4	2022.889 2
本文算法	0.970 6%	0.903 4%	0.935 9%	12.722 9	639.933 5

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