# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 1 Introduction
argued that, in addition to periodic signals, there was also interference noise caused by imperfections in the reference PRNU noise extraction method that was correlated with the image content leaked in the reference PRNU noise. They proposed a method to suppress these interference noises by decorrelation. The analysis above shows that the PRNU can be processed in the frequency domain to improve its quality. The leaked scene details in the reference PRNU noise maintain a certain local image structure [10], which is the most important factor causing the neighbouring pixel blocks of the reference PRNU to be correlated. In other words, the ideal reference PRNU noise should not have a local image structure, and the local pixel blocks should be uncorrelated with each other. Therefore, the scene details in the reference PRNU noise can be estimated based on the correlation of the neighbouring pixel blocks. Unless otherwise stated, the rest of this paper refers to the scene details in the reference PRNU noise as interference noise. In this paper, we proposed an enhancement scheme based on principal component analysis (PCA) to remove the interference noise in the reference PRNU noise. The proposed scheme proceeded in two steps. First, PCA was used to estimate the interference noise in the reference PRNU. In order to accurately estimate the interference noise, we designed an effective data selection strategy to construct training samples in each local pixel block for PCA. Additionally, we designed appropriate shrinkage coefficients to retain the interference noise in each component of the PCA domain. Finally, the enhanced reference PRNU was obtained from the noise residual between the reference PRNU and the estimated interference noise. This paper proposed a novel scheme based on the PCA technique to enhance the extraction quality of reference PRNU noise. As a result, the performance of digital image source camera identification can be improved. The main contents of the manuscript are given below. * We provided a novel technique to maximize the quality of reference PRNU noise and to effectively remove the content left by the image from the reference PRNU noise. Unlike the prior works, the proposed scheme estimates interference noise in the reference PRNU noise in the PCA domain. * To effectively remove the interference noise caused by image details, we proposed selecting pixel blocks with high similarity in a local region to participate in PCA training. The pixel blocks with comparatively high similarity contained more image details. Furthermore, we used an appropriate shrinkage function in the PCA domain to further refine the image details. The proposed scheme effectively estimated the image details in the reference PRNU noise and achieved the goal of enhancing the reference PRNU noise through residual operations. * We conducted sufficient experiments on the most classical dataset. By comparing with the prior works, we validated that our proposed scheme achieved the enhancement of reference PRNU noise, effectively improving the performance of the digital image source camera detection technology. The rest of this paper is organized as follows. In Section 2, the related work on PRNU is briefly introduced. In Section 3, we describe the proposed scheme in detail. Section 4 shows the experimental results and analysis, followed by the conclusions in Section 5.