## 4 Numerical Results
### Datasets Used  
In this paper, we used six datasets: the Dresden [24] dataset, which has been extensively used in previous camera forensics research, and five datasets containing smartphone cameras (ALASKA [26]; SOCRatES [30]; Forchheim [31]; SIHDR [32]; VISION [33]), three of which also contain images that have been processed by multiple online social networking platforms. The smartphone camera dataset was particularly important considering the rapid growth and popularity of today's online social networking platforms, as well as the fact that the vast majority of captured images come from smartphones. We summarize the relevant content and features of each dataset below. The Dresden [24] dataset comprises over 16,000 images captured using 73 distinct digital cameras across 25 camera models. This classical dataset showcases a variety of lighting conditions and diverse scene compositions, including both indoor and outdoor environments, as well as settings featuring public places and natural elements like trees. To better simulate real-world scenarios, the dataset incorporates a range of camera settings, such as variations in focal length and the use of flash. To validate the performance of our proposed distributed source camera identification scheme, we used real JPEG images from the Dresden dataset to conduct experiments. All images were captured at the highest resolution and maximum JPEG quality factor. We randomly selected different camera models for the experiment and used all images from the selected camera models. The Alaska [26] dataset provides a comprehensive collection of 80,000 images captured by over 40 different cameras, ranging from smartphones and tablets to both low-end cameras and high-end full-frame digital single-lens reflex models. The dataset is designed to reflect a wide variety of real-world scenarios, encompassing highly heterogeneous image processing conditions. To facilitate use, especially for various study tasks, Alaska offers several preprocessed subsets. These subsets include uncompressed color and grayscale images available in standardized sizes of 512 \(\times\) 512 and 256 \(\times\) 256 pixels, as well as JPEG-compressed images with varying quality factors, ranging from 75 to 100. This diversity allows for flexibility in experimenting with different compression levels and resolutions, making the dataset a versatile tool for research in image processing and forensic analysis. The SOCRatES [30] dataset is a comprehensive dataset consisting of approximately 9700 images, captured using 103 different smartphones from 15 different brands and around 60 unique models. What sets SOCRatES apart from previously published datasets is its unique data collection process, where smartphone owners themselves captured the images, introducing a high degree of diversity and realism. With its wide range of 103 devices, SOCRatES holds the distinction of being the largest dataset for source camera identification in terms of sensor variety. Specifically designed to support the development and benchmarking of image forensic techniques on smartphones, this dataset is particularly valuable for addressing the source camera identification problem, although its applications extend beyond this. The Forchheim [31] dataset comprises approximately 4000 images sourced from 25 distinct smartphone camera models. This extensive collection showcases a diverse range of scene content and varying capture conditions, including indoor versus outdoor settings, day versus night scenarios, and close-up versus distant shots. By capturing multiple images of the same scene across different devices, the dataset effectively minimizes the influence of content variability, which can often obscure camera model identification processes. Furthermore, the dataset includes versions of these images that have undergone post-processing via five popular social media platforms (i.e., WhatsApp, Facebook, Instagram, Telegram, and Twitter).