## 2 Problem Formulation and System Model  
### Problem Formulation  
With the increasing prevalence of digital images, source camera identification has gained critical importance in the field of image forensics [20]. Digital images are now widely used as digital evidence by various official entities, including courts of law, government agencies, police investigations, and news outlets, playing a significant role in their decision-making processes [21]. Due to the rapid development of network technology and the great advances in image editing tools, illegal attackers can easily edit, alter, or forge images,leading to questions about the reliability and trustworthiness of digital images [22]. As a result, the need for reliable methods to verify the authenticity and source of digital images has become more urgent than ever. Source camera identification techniques serve as an essential tool for determining the specific camera model or device that captured an image, ensuring the authenticity and trustworthiness of digital evidence. However, traditional identification methods usually make a decision by analyzing the image only once, which may lead to missed detections or false alarms. To address the above limitations, we propose a novel distributed source camera identification framework. This approach enhances the accuracy and robustness of the identification process by leveraging the collaborative efforts of multiple secondary users. Through this distributed framework, we aim to provide a more reliable method for determining the source of digital images, ultimately improving the validity of digital evidence in critical applications. As shown in Figure 1, we design a distributed source camera identification framework containing a central classifier and three secondary users. All three secondary users are assumed to be trustworthy. To compare with the fingerprint of the inquiry image, each secondary user first separately extracts the camera fingerprint from images taken by a specific known camera model, where the images used to extract the camera fingerprint are different for each secondary user. Then, each secondary user individually makes a preliminary judgment for the same inquiry image by using a specialized source camera identification technique (i.e., the GLRT designed in Section 3.2.2) to evaluate whether the image is from a specific known camera model. The secondary users send their respective binary judgments (1 means the image is from a known camera, 0 means the image is not from a known camera) to the central classifier. The central classifier receives and fuses the decision information from all the secondary users and makes the final judgment based on the \"\(n\)-out-of \(K^{n}\) voting rule (Section 3.1) to infer whether the image is from a specific camera model. As illustrated in Figure 2, we present a real-world application scenario demonstrating the process by which a single secondary user identifies the source camera of an image. In such cases, digital images may be transmitted as evidence to official institutions, such as courts, particularly in sensitive cases like military operations or child pornography. Indeed, malicious attackers can potentially tamper with or falsify digital images, thereby compromising the credibility of the images. Consequently, when images are used as  
Figure 1: Distributed source camera identification with multiple secondary users. evidence, it becomes essential for courts to verify the authenticity of the image source to uphold the integrity of the digital evidence. However, courts typically lack the expertise and computational resources required for source camera identification. At this point, engaging external experts is regarded as an effective solution to the above problems [23]. Nevertheless, external experts are not official entities, and thus relying on the experts may expose inquiry image information to potential malicious attackers, increasing the risk of unauthorized disclosure.