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

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 coding model,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.

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.