Research of Image Authenticity Detection Method Based on Feature Matching

NAVIGATE

Table of Contents

STATISTICS

Viewed1935

Downloads774

Research of Image Authenticity Detection Method Based on Feature Matching

[HTML] PDF下载 (774)

[1]HUA Bei,CHEN Qian,HUANG Ruwei.Research of Image Authenticity Detection Method Based on Feature Matching[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):22-27.[doi:10.13705/j.issn.1671-6833.2022.02.001]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 02 Page number: 22-27 Column: Public date: 2022-02-27

Title:: Research of Image Authenticity Detection Method Based on Feature Matching

Author(s):: HUA Bei; CHEN Qian; HUANG Ruwei; School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China

Keywords:: image tamper detection; copy-move tampering; feature matching; mathematical morphology; tamper area location

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2022.02.001

Abstract:: In view of the problem that the tamper detection algorithm based on SIFT feature matching had， such as unclear location of tamper region and interference of SIFT mismatch on tamper region resolution, a method of image tamper location based on mathematical morphology and feature matching was proposed. The method detects the extreme value of the image in the scale space, locate the key points of the image features, determine the gradient direction of the image, and form the feature point descriptor. Then based on the Euclidean distance, the feature descriptors were matched by two matching blocks, the tamper region is roughly located, and the redundant matching block was removed by mathematical morphology. SIFT feature point of suspected region pairs are extracted, the feature points were matched after PCA dimensionality reduction of SIFT feature matrix, and the matched feature points were purified by using the correlation coefficient. Finally, according to the distribution of matching feature points, the tampered area is relocated to accurately obtain the copy-move tamper area. In the experiment, a group of representative images were selected and detected after adding copy-move, rotation and zooming attacks. The results showed that the prorosed method could locate the location of the copy-move tamper area more accurately, and had higher accuracy in the image copy-move tamper detection.

References:: ［1］王金磊．基于 SIFT 关键点的新闻图像复制粘贴篡改检测算法［J］．中国传媒科技，2020( 12) : 117－118．
［2］葛琳琳，韦梦文，张威，等．基于混沌映射图像篡改检测的脆弱水印方案［J］．辽宁石油化工大学学报， 2020，40( 6) : 91－96．
［3］ MAHMOOD T，MEHMOOD Z，SHAH M，et al． A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform［J］．Journal of visual communication and image representation，2018，53: 202－214．
［4］ SADEGHI S，JALAB H A，WONG K，et al． Keypoint based authentication and localization of copy-move forgery in digital image［J］．Malaysian journal of computer science，2017，30( 2) : 117－133．

［5］ SAINI G K，MAHAJAN M．Improvement in copy-move forgery detection using hybrid approach ［J ］． International journal of modern education and computer science，2016，8( 12) : 56－63．

［6］ SELVAＲAJ P，KAＲUPPIAH M． Enhanced copy-paste forgery detection in digital images using scale-invariant feature transform［J］． IET image processing，2020，14 ( 3) : 462－471．

［7］ WU Y，ABDALMAGEED W，NATAＲAJAN P．ManTranet: manipulation tracing network for detection and localization of image forgeries with anomalous features ［C］/ /2019 IEEE/CVF Conference on Computer Vision and Pattern Ｒecognition ( CVPＲ) ． Piscataway: IEEE，2019: 9535－9544．

［8］张震，苏白娜，喻宙．一种新的图像拷贝篡改检测方法［J］．郑州大学学报( 工学版) ，2012，33( 6) : 15－18．

［9］李子健，阮秋琦．基于 LPP 和改进 SIFT 的 copy-move 篡改检测［J］．信号处理，2017，33( 4) : 589－594．

［10］马伟鹏，林敏锐，吴泽宇，等．基于 SIFT 和感知哈希的图像复制粘贴篡改检测方法［J］．现代计算机， 2019( 15) : 56－59．

［11］巩家昌．基于局部颜色不变性特征的图像复制粘贴篡改取证研究［D］．天津: 天津大学，2016．

［12］汪涛．数字图像复制粘贴篡改的被动认证方法研究［D］．合肥: 安徽大学，2014．

［13］骆伟祺，黄继武，丘国平．鲁棒的区域复制图像篡改检测技术［J］．计算机学报，2007，30 ( 11) : 1998 －2007．

［14］柴新新，邱晓晖．基于提升小波变换的图像篡改检测算法［J］．计算机技术与发展，2016，26( 4) : 78－81．

［15］ LOWE D G． Distinctive image features from scale-invariant keypoints［J］． International journal of computer vision，2004，60( 2) : 91－110．

［16］ SOILLE P．形态学图像分析原理与应用［M］．王小鹏，等译．2 版．北京: 清华大学出版社，2008．

［17］王俊文，刘光杰，张湛，等．图像区域复制篡改快速鲁棒取证［J］．自动化学报，2009，35 ( 12) : 1488 －1495．

［18］张宇，刘雨东，计钊．向量相似度测度方法［J］．声学技术，2009，28( 4) : 532－536．

［19］ ZHOU P，HAN X T，MOＲAＲIU V I，et al． Learning rich features for image manipulation detection［C］/ / 2018 IEEE/CVF Conference on Computer Vision and Pattern Ｒecognition． Piscataway: IEEE，2018: 1053 －1061．

Similar References:

Memo

Last Update: 2022-02-25