# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 1 Introduction  
The development of digital imaging technology has been changing people's way of life [1, 2]. People are increasingly willing to share their lives through taking photos, and the lack of public awareness on information protection greatly increases the risk of image theft and malicious use. Millions of photos are captured, transmitted, and saved every day, many of which are used in sensitive and important applications. Digital forensics technology effectively safeguards the personal interests of citizens [3, 4]. We focus our attention on the exploration of source camera forensic technology. The information about source camera included in the file headers of digital images can be easily stripped and tampered [5]. It is imperative for us to guarantee the authenticity of the image being used in a sensitive scenario. In this light, the source camera identification (SCI) problem has attracted much attention from researchers. Source camera identification determines the source camera of a test image by analysing its unique features. A camera sensor inevitably leaves traces in images due to the imperfection of material and the manufacturing process. In the literature, the traces are often called sensor patternnoise (SPN). Since each camera's sensor is different, its pattern noise is also unique, so the sensor pattern noise can be treated as a \"fingerprint\" of the camera. [PERSON] et al. [6] first proposed a source detection algorithm for camera images based on SPN. They proved that SPN is a kind of zero-mean noise signal, mainly caused by non-uniformity of silicon wafer. The size of SPN is consistent with that of the sensor. SPN is mainly composed of photo response non-uniformity (PRNU) noise. Therefore, SPN refers to PRNU throughout the rest of this paper unless otherwise stated. The effectiveness of the PRNU-based SCI algorithm depends on the quality of PRNU noise extracted from images. Following a standard procedure to associate an image with the source camera, the PRNU noise extracted from the test image must be compared with the reference PRNU of the camera. The reference PRNU is obtained by averaging the PRNU noises from a set of sample images captured by the camera. Improving the quality of the reference PRNU is also called PRNU enhancement [7], which further removes interference noise from the reference PRNU that is useless for forensics, thereby achieving improvement of SCI accuracy. The study of PRNU enhancement has attracted the attention of many experts and scholars. [PERSON] et al. [7] pointed out for the first time that the estimated PRNU noise contains some artificial trace noise, which is caused by the processing algorithm embedded in the image acquisition process, such as colour filter array (CFA) interpolation, gamma correction, and JPEG compression. These artificial trace noises cannot be used in SCI and should be removed from the reference PRNU noise. [PERSON] et al. used a zero-mean operation to remove the noise introduced by CFA interpolation and performed Wiener filtering of the estimated PRNU noise in the Fourier transform domain. Based on the assumption that the reference PRNU noise can be modelled as a WGN, [PERSON] and [PERSON] [8] proposed a spectrum equalization algorithm (SEA) enhancement method, which first used an iterative approach to calculate periodic abnormal peaks in the spectrum domain due to JEPG compression. After locating the periodic compression-related peaks in the spectrum domain, the local mean was used to smooth and suppress the abnormal peaks, so as to eliminate the unwanted noise. Recently, [PERSON] et al. [9] indicated that although SEA can successfully eliminate most periodic interfering signals in the spectrum, the SEA spectrum was not very flat and most of the energy was concentrated in the low-frequency components, and its spectral distribution was not consistent with white Gaussian noise (WGN). [PERSON] et al.