# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 1 Introduction
In [7], the authors present source camera identification via image texture features that are extracted from well-selected color models and color channels, and the proposed method is superior in both detection accuracy and robustness than the other methods. In [8], by considering the image texture, the authors propose to design a  
Figure 1: Image acquisition pipeline in typical camera devices. new classifier by adopting a weight function, leading to the remarkable reduction of the feature dimensionality. In examining the differences in lens distortion parameters of different brands or models of cameras, [PERSON] et al. proposed a source camera identification method based on the lens distortion correction interpolation attribute. Sensor pattern noise (SPN) is the most serious sensor artifact [9]. It consists of two main parts: fixed pattern noise (FPN) and photo response non-uniformity (PRNU). The method presented in [10] uses a wavelet denoising filter to extract the pattern noise of images; the method presented in [11] is applied for estimation of camera fingerprints by averaging a large amount of reference image noise to suppress random noise components and contamination effects. The features related to software are the color filter array (CFA) interpolation algorithm, joint photographic experts group (JPEG) compression algorithm, white balance algorithm, and gamma correction. The method presented in [12] established a search space with 36 possible CFA modes and estimated the interpolation coefficients by fitting a linear filtering model in various texture regions of the image for each CFA mode \(P\) in search space \(p\). The method presented in [13] proposed a new method based on the basic principle of color interpolation to estimate the CFA mode of a digital camera from a single image. Through a detailed imaging model and its component analysis, the method presented in [14] estimated the intrinsic fingerprint of various camera processing operations. Another category of source camera identification methods is based on deep learning, which uses CNNs to automatically extract useful features and then classify them using classifiers. The CNN's powerful feature extraction ability makes it outstanding in computer-vision-related tasks. Therefore, many researchers have attempted to apply deep learning methods to the field of image forensics and achieved good results. [PERSON] et al. in [15] divided an image into several image patches and classified the source camera of each patch. Finally, according to the voting rule, the camera device with the most image patches was selected as the source camera of the image to be tested. [PERSON] divided an image into three types (smooth, saturated, and others) according to the image content, and then used a content adaptive residual network to classify the image source to determine the camera equipment to which the image belongs [16]. [PERSON] et al. proposed a network similar to Alexnet for image source detection, which is superior to the classical networks Alexnet and Googlenet in camera model detection, and obtained a better detection effect [17]. After AlexNet was introduced in 2012, it won the championship of the Large-Scale Visual Recognition Challenge (ILSVRC). The CNN has attracted the attention of many researchers. In the following years, deep-learning made amazing achievements in image classification [18; 19; 20], object detection [21; 22; 23], image denoising [24; 25], and information security [26]. Due to the strong feature extraction ability of convolutional neural networks and the excellent performance obtained by those techniques on many fields, researchers attempted to apply deep learning to image forensics and achieve better performance than the traditional artificial feature extraction algorithm. For the above reasons, we chose the deep learning scheme for camera source identification. The application of deep learning in image source forensics includes the following three aspects:  
1.