# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 1 Introduction  
With the rapid development of the new generation of information technology represented by the Internet, big data and artificial intelligence, networking, digitization and intellectualization have become the trend of the times, and digital images have been integrated into all aspects of social life. People can easily use mobile phones or cameras to capture pictures and then use commonly used image editing software to tamper with the image content to spread rumors, commit economic fraud, and other criminal activities. The spread of false pictures is becoming increasingly widespread. As a result, an increasing number of people are losing confidence in the authenticity of digital images and think that images are not reliable information carriers [1, 2]. To fight against the crime of fake pictures and rebuild people's trust in image information, digital image forensics has become a research hotspot in recent years. Source camera identification is an important part of digital image forensics, which works to determine from which camera a digital image originated. In addition, source camera identification has a high application value in tracking the source of pornographic images, art photo copyright authentication, and so on. In the past decade, a large number of algorithms for source camera identification has emerged. Although their principles and methodsare different, they all have one thing in common: to extract some traces introduced by human or equipment defects in the image-shooting process and then determine the image acquisition equipment according to these traces. Therefore, let us briefly introduce the general forming process of digital images. The image-forming process is shown in Figure 1. As shown in the Figure, the light on the surface of the object is projected to the surface of the photosensitive elements Charge Coupled Device (CCD) and Complementary Metal-Oxide (CMOS) through the lens. The light is decomposed into different colored lights by filters on the photosensitive elements. The colored lights are sensed by the corresponding photosensitive units of each filter, and analog current signals of different intensities are generated, which are then converted into digital signals by analog to digital conversion (ADC). Finally, digital signal processing (DSP) carries out color correction and white balance processing, and encodes and compresses these image data into digital images. The formation of digital images requires multiple information processing links involving a series of image-related software processing programs and optical components. However, there are differences in software algorithms and optical components used by different manufacturers or models of cameras. Researchers utilize different techniques to discover traces left by every hardware component or software process during image formation on the image content. These traces are known as intrinsic image artifacts. The existing source camera identification algorithms can be divided into two categories. The first extrac features manually, and then compares similarities. The features related to hardware are artifacts caused by optical and sensor defects. [PERSON] et al. discovered for the first time that the lens radial distortion (LRD) level is dissimilar in different lens manufacturing designs and that this value changes depending on the focal length of the camera lens [3]. A short focal length suffers from more barrel distortion, while a long focal length suffers from further pillow distortion. In [4; 5], the authors employed the straight-line method to estimate the LRD parameters. They improved the accuracy of camera attributes by combining the estimated parameters with basic statistical features [6], trying to capture the photometric artifacts and geometric artifacts left by the color-processing algorithm in the image.