基于超像素分割的图像复制粘贴篡改检测

doi:10.3969/j.issn.0255-8297.2019.03.012

应用科学学报 ›› 2019, Vol. 37 ›› Issue (3): 419-426.doi: 10.3969/j.issn.0255-8297.2019.03.012

基于超像素分割的图像复制粘贴篡改检测

刘佳睿¹, 卢伟^1,2, 刘轲¹, 黄信朝¹, 蔺聪¹, 刘先进¹

1. 中山大学数据科学与计算机学院, 广州 510006;
2. 中科院信息工程研究所信息安全国家重点实验室, 北京 100093

收稿日期:2018-05-08 修回日期:2018-09-03 出版日期:2019-05-31 发布日期:2019-05-31
通信作者: 卢伟,副教授,研究方向:数字取证,E-mail:luwei3@mail.sysu.edu.cn E-mail:luwei3@mail.sysu.edu.cn
基金资助:
国家自然科学基金（No.U1736118）；广东省自然科学基金（No.2016A030313350）；广东省科技发展专项基金（No.2016KZ010103）；广州市科学研究计划重点项目基金（No.201804020068）；上海市民生科技支撑计划基金（No.17DZ1205500）；上海市启明星计划基金（No.17YF1420000）；中央高校基本科研业务费（No.16lgjc83，No.17lgjc45）资助

Copy-Move Forgery Detection Based on Super Pixel Segmentation

LIU Jiarui¹, LU Wei^1,2, LIU Ke¹, HUANG Xinchao¹, LIN Cong¹, LIU Xianjin¹

1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;
2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Received:2018-05-08 Revised:2018-09-03 Online:2019-05-31 Published:2019-05-31

摘要/Abstract

摘要： 提出了一种基于超像素分割的结果进行聚类来检测复制粘贴篡改区域的方法.常规K-means等点聚类方法是直接对点进行聚类分析，而该方法则是将若干随机种子置于图像中，借助于超像素分割方法进行区域分割，随后获得包含预先匹配特征点的区域.所提算法以此区域内匹配特征点的数目作为衡量标准，判定区域内的匹配特征点是否为有效特征点.当匹配点的数目到达某个阈值时则将子区域内的点聚为一类，这样聚类的结果更加贴近图像内容，符合实际情况.实验结果表明，所提方法比传统的SIFT等方法更加有效.

关键词: 数字图像取证, 尺度不变特征变换, 复制粘贴篡改, 超像素分割

Abstract: A clustering method to detect the copy-move area based on the results of superpixel segmentation is proposed. Different from the traditional K-means clustering method, the proposed clustering method is to place random seeds in the image and segment the region by using super-pixel segmentation method, and then obtain the regions containing pre-matched feature points. In this algorithm, the number of matched feature points in each region is used as a criterion to determine whether the matched feature points in the region are effective feature points. When the number of matching points reaches a certain threshold, the points in the sub-regions are clustered into one group, so that the clustering results are closer to the image content and in accordance with the actual situation. Experiments show that the proposed method based on super-pixel segmentation is more effective than the traditional SIFT method.

Key words: scale invariant feature transform (SIFT), super pixel segmentation, digital image forensics, copy-move forgery

中图分类号:

P751.1

刘佳睿, 卢伟, 刘轲, 黄信朝, 蔺聪, 刘先进. 基于超像素分割的图像复制粘贴篡改检测[J]. 应用科学学报, 2019, 37(3): 419-426.

LIU Jiarui, LU Wei, LIU Ke, HUANG Xinchao, LIN Cong, LIU Xianjin. Copy-Move Forgery Detection Based on Super Pixel Segmentation[J]. Journal of Applied Sciences, 2019, 37(3): 419-426.

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基于超像素分割的图像复制粘贴篡改检测

Copy-Move Forgery Detection Based on Super Pixel Segmentation

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