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基于数据降维与对称二值模式的图像Hash算法

Image Hash Algorithm Based on Data Dimension Reduction and Symmetric Binary Pattern

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摘要

为了解决当前图像Hash算法难以兼顾较高的感知稳健性与篡改识别率的不足, 提出了基于数据投影降维机制与对称局部二值模式的紧凑图像Hash算法。利用双线性插值来预处理图像, 使Hash具有固定的长度; 引入对数极坐标变换, 将其转变为二次图像; 利用Gabor滤波器平滑二次图像; 基于模糊集理论, 设计对称局部二值模式算子, 获取稳健特征; 定义数据投影降维机制与量化规则, 生成紧凑的中间Hash比特序列; 构造一维组合混沌映射, 建立加密模型, 完成比特序列扩散, 以生成图像Hash; 并引入汉明距离, 估算初始图像与接收端图像的Hash相似度, 联合决策阈值, 完成图像认证。测试数据表明, 与当前图像Hash技术相比, 该算法的Hash更紧凑, 且其感知稳健性与敏感性更高。

Abstract

In order to solve the problem of difficulty of both the high perception robustness and tampering identification rate in the current image Hash algorithm, the compact image Hash algorithm based on data projection dimension reduction mechanism and fuzzy symmetric local binary pattern is proposed. The generated Hash has a fixed length by introducing the bilinear interpolation mechanism to preprocess the image. And the pretreatment image is transformed into the secondary image by the log polar transformation. The secondary image is smoothed by Gabor filter. The fuzzy symmetric local binary pattern operator is designed based on the fuzzy theory. And the compact intermediate Hash sequence is got by defining the data projection dimension reduction mechanism. The image Hash is generated by diffusing the bit Hash based on designing the one-dimensional combined chaotic map. The similarity between the original image and the image of the receiving end is estimated by introduction the Hamming distance and decision threshold to finish the authentication of image. Testing data show that this algorithm has stronger perception robust and sensitivity with tighter Hash length than the current image Hash technologies.

补充资料

中图分类号:TP391。4

DOI:

所属栏目:图像处理

基金项目:河南省科技计划重点项目(102102210416)、河南省软科学研究计划项目(152400410323)

收稿日期:2016-09-22

修改稿日期:2016-10-10

网络出版日期:--

作者单位    点击查看

王彦超:平顶山教育学院计算机系, 河南 平顶山 467000
郭静博:平顶山教育学院计算机系, 河南 平顶山 467000
周丽宴:郑州大学信息工程学院, 河南 郑州 450001

联系人作者:王彦超(WangYchao1975pds@163.com)

备注:王彦超(1975-)男, 硕士, 副教授, 主要从事图像图形处理、模式识别、虚拟化技术方面的研究。

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引用该论文

Wang Yanchao,Guo Jingbo,Zhou Liyan. Image Hash Algorithm Based on Data Dimension Reduction and Symmetric Binary Pattern[J]. Laser & Optoelectronics Progress, 2017, 54(2): 021004

王彦超,郭静博,周丽宴. 基于数据降维与对称二值模式的图像Hash算法[J]. 激光与光电子学进展, 2017, 54(2): 021004

被引情况

【1】张智丰,裴志利. 一种稳健的紧凑图像哈希算法. 激光与光电子学进展, 2017, 54(10): 101002--1

【2】谢欣芳,徐新,董浩,吴晗,李珞茹. 一种极化SAR影像分类中的半监督降维方法. 光学学报, 2018, 38(4): 428001--1

【3】彭晏飞,武宏,訾玲玲. 基于哈希算法及生成对抗网络的图像检索. 激光与光电子学进展, 2018, 55(10): 101002--1

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