采用局部强度顺序模式的图像复制—粘贴篡改检测算法

doi:10.11959/j.issn.1000-436x.2016259

Journal on Communications ›› 2016, Vol. 37 ›› Issue (Z1): 132-139.doi: 10.11959/j.issn.1000-436x.2016259

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Detection of image copy-move forgery using local intensity order pattern

Jing LIN¹,Tian-qiang HUANG^2,³(),Ling-peng LIN^2,³,Xiao-chen1 LI¹

¹ School of Mathematics and Computer Science,Fujian Normal University,Fuzhou 350007,China
² Faculty of Software,Fujian Normal University,Fuzhou 350007,China
³ Fujian Provincial University Engineering Research Center of Big Data Analysis and Application,Fujian Normal University,Fuzhou 350007,China

Online:2016-10-25 Published:2017-01-17
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;Industry-University Coopera-tion Major Projects in Fujian Province;Science and Technology Program of Fujian;Program for New Century Excellent Talents in University in Fujian Province;The Science and Technology Department of Fujian Province K-Class Foundation Project;The Education Department of Fujian Province A-Class Foundation Project;The Graduate Education Reform Project of Fujian Normal University

Abstract

Abstract:

Copy-move forgery was one of the most simple and common way of image manipulations.To improve the ro-bustness of most existing copy-move forgery detections,a new method based on local intensity order pattern was pro-posed.First,the LIOP feature descriptors were exacted from the inspected image.Then the angular cosine of feature de-scriptors were used to measure the similarity,and the stable matching points were found according to the distance ratio threshold of the nearest neighbor point to the second nearest neighbor.Finally,the space distance of the matching points were calculated to remove the false matching points.Extensive experimental results were presented to confirm that the proposed method is not only able to effectively identify and locate the altered area,but also have high accuracy and ro-bust to scaling,rotation,brightness change and some post-processing,such as Gaussian blur,additive white Gaussian noise and JPEG compression.

Key words: image tampering detection, copy-move forgery, feature description, local intensity order pattern

Jing LIN,Tian-qiang HUANG,Ling-peng LIN,Xiao-chen1 LI. Detection of image copy-move forgery using local intensity order pattern[J]. Journal on Communications, 2016, 37(Z1): 132-139.

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References 14

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算法	缩放	旋转	亮度调整	高斯模糊	加性高斯白噪声	JPEG压缩
文献[6]算法	77.78%	92.48%	60.78%	85.62%	85.77%	75.24%
文献[8]算法	92.37%	90.74%	72.45%	67.75%	92.22%	94.19%
本文算法	96.10%	96.27%	100%	100%	96.68%	98.61%

算法	缩放	旋转	亮度调整	高斯模糊	加性高斯白噪	JPEG压缩
文献[6]算法	50.47%	90.65%	49.06%	69.05%	70.60%	88.69%
文献[8]算法	80.97%	90.22%	54.39%	30.86%	82.86%	87.53%
本文算法	91.36%	95.59%	78.14%	75.67%	84.12%	94.44%

算法	特征维数	准确率	召回率	运行时间/s
文献[6]算法	12	76.84%	82.80%	8.77
文献[8]算法	128	87.97%	76.50%	3.39
本文算法	144	97.56%	91.27%	5.48

Detection of image copy-move forgery using local intensity order pattern

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