基于三方加权稀疏编码模型的PRNU提取算法

doi:10.3969/j.issn.0255-8297.2019.05.011

应用科学学报 ›› 2019, Vol. 37 ›› Issue (5): 704-710.doi: 10.3969/j.issn.0255-8297.2019.05.011

基于三方加权稀疏编码模型的PRNU提取算法

张永胜^1,2, 田华伟^1,2, 肖延辉^1,2, 郝昕泽^1,2, 张明旺³

1. 中国人民公安大学国家安全与反恐怖学院, 北京 100038;
2. 中国人民公安大学公安情报研究中心, 北京 100038;
3. 四川警察学院科研所, 四川泸州 646000

收稿日期:2019-07-27 修回日期:2019-08-01 出版日期:2019-09-30 发布日期:2019-10-18
通信作者: 田华伟,副教授,研究方向:信息隐藏与多媒体取证,E-mail:hwtian@live.cn E-mail:hwtian@live.cn
基金资助:
国家自然科学基金（No.61772539，No.6187212，No.61972405）；四川省科技计划项目（No.2018JY0521）；公安部技术研究计划（No.2017JSYJC01）；四川省泸州市科技局项目（No.2018-GYF-8）资助

PRNU Extraction Algorithm Based on Trilateral Weighted Sparse Coding Model

ZHANG Yongsheng^1,2, TIAN Huawei^1,2, XIAO Yanhui^1,2, HAO Xinze^1,2, ZHANG Mingwang³

1. School of National Security and Counter Terrorism, People's Public Security University of China, Beijing 100038, China;
2. Research Center for Public Security Information, People's Public Security University of China, Beijing 100038, China;
3. Institute of Research, Sichuan Police College, Luzhou 646000, Sichuan Province, China

Received:2019-07-27 Revised:2019-08-01 Online:2019-09-30 Published:2019-10-18

摘要/Abstract

摘要： 估计图像中真实噪声是基于光照响应不一致（photo-response non-uniformity， PRNU）对图像来源取证的关键步骤.相较于加性高斯白噪声（additive white Gaussian noise， AWGN）的估计，现有多数PRNU提取算法所采用的噪声估计算法在图像真实噪声提取方面性能劣势明显.该文提出了一种基于三方加权稀疏编码模型（trilateral weighted sparse codingmodel，TWSCM）的PRNU提取算法.TWSCM在估计噪声时能够保留更多PRNU噪声成分，有助于对图像中PRNU噪声的提取，因此在真实噪声估计上具有较好的性能.在当前最大的图像相机源取证基准库上的测试，实验结果证明所提出的基于TWSC的PRNU提取算法在图像相机源取证任务中具有较好的性能.

关键词: 图像来源取证, 光照响应不一致, 真实图像噪声估计

Abstract: Estimating the real noise of real-world image is the most important issue of image source forensics based on photo-response non-uniformity (PRNU). Compared with the estimation of additive white Gaussian noise (AWGN), most exsiting noise estimation algorithms used in PRNU extraction behave with poor satisfaction in estimating real noise. In this paper, we propose a PRNU extraction algorithm based on trilateral weighted sparse coding model (TWSCM). TWSCM has advantage in estimating the real noise of real-world image, because it can keep more PRNU noise in the estimation results. Having been tested on the largest image source forensics database, the proposed TWSCM-based PRNU extraction algorithm outperforms the existing algorithm of source forensic.

Key words: image source forensic, photo-response non-uniformity (PRNU), real-word image denoising

中图分类号:

TP391

张永胜, 田华伟, 肖延辉, 郝昕泽, 张明旺. 基于三方加权稀疏编码模型的PRNU提取算法[J]. 应用科学学报, 2019, 37(5): 704-710.

ZHANG Yongsheng, TIAN Huawei, XIAO Yanhui, HAO Xinze, ZHANG Mingwang. PRNU Extraction Algorithm Based on Trilateral Weighted Sparse Coding Model[J]. Journal of Applied Sciences, 2019, 37(5): 704-710.

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基于三方加权稀疏编码模型的PRNU提取算法

PRNU Extraction Algorithm Based on Trilateral Weighted Sparse Coding Model

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