## 1 Introduction  
With the rapid development of smart cities, massive amounts of data are being generated across various sectors, including judicial, governmental, healthcare, elderly care, and transportation systems [1]. Among these diverse data types, digital images play a vital role and are extensively used as digital evidence in urban management scenarios such as video surveillance, news reporting, and legal proceedings. However, the advancement of image processing technologies has significantly lowered the barriers for malicious actors to manipulate or forge digital images. This growing vulnerability underscores the increasing importance of source camera identification, which enables the verification of an image's origin and contributes to ensuring its authenticity and integrity [2]. Source camera identification approaches are generally divided into two categories. The first category is the active approach, which relies on information embedded in the image to verify its source. [PERSON] et al. proposed a lossless authentication watermarking framework [3]. [PERSON] et al. developed a lossless visible watermarking scheme considering human visual features [4], and [PERSON] et al. introduced a chaotic watermarking scheme based on semi-fragile watermarking [5]. The second category is the passive forensic approach, which relies on camera fingerprints, such as pattern noise (e.g., photo response non-uniformity (PRNU) noise), to identify the source camera. This noise arises from sensor responses and internal image signal processing [6,7]. [PERSON] et al. detected image demosaicing regularities [8], and [PERSON] et al. proposed a spatial domain averaging technique to enhance efficiency by reducing denoising times [9]. Other works, such as those by [PERSON] et al. [10], [PERSON] et al. [11], and [PERSON] et al. [12], have focused on suppressing correlated noises, using heteroscedastic noise models, and incorporating privacy-preserving methods to improve source camera identification performance. Although the above methods contribute significantly to source camera identification, active approaches relying solely on embedded information are often considered unreliable [11], while passive methods dependent on intrinsic features may fail to achieve sufficient accuracy under complex image processing techniques [13]. To address these challenges, we propose a novel hybrid fingerprint model that integrates both active and passive forensic approaches, combining embedded information with intrinsic camera features. The proposed method improves the reliability and accuracy of source camera identification by taking advantage of the complementary strengths of the active approach and the passive forensic approach, thus overcoming the limitations of each when used independently. Unlike existing methods, our hybrid fingerprint model adapts to varying image processing scenarios, providing a more robust and accurate solution for source camera identification. Traditional source camera identification methods usually make a judgment about images by performing the identification process only once [14, 15, 16]. However, modern image-processing techniques, such as tampering and compression, pose significant challenges to the reliability of such methods. Furthermore, the privacy of sensitive images, such as those involving military, political, or personal data, must also be safeguarded during the identification process. To address these issues, we propose a distributed source camera identification scheme with a privacy-preserving strategy, which not only improves reliability but also protects sensitive information. In the digital era, the widespread use of image acquisition devices and social media platforms facilitates the sharing of digital images, but post-processing operations such as image scaling and JPEG compression [17, 18, 19] can degrade camera fingerprints, reducing the effectiveness of traditional identification methods. Therefore, it is crucial to consider the impact of such processing techniques on source camera identification.