Recent Advances in Deep Learning-Based Source Camera Identification and Device Linking

Photo-response non-uniformity (PRNU) has long been regarded as a reliable method for source camera identification and device linking in forensic applications. Recent advances in deep learning (DL) have introduced diverse architectures, including convolutional neural networks, residual learning, encoder–decoder representations, dual-branch structures, and contrastive learning, to capture specific sensor artifacts. This review summarizes the performance of these DL techniques across both tasks and compares their effectiveness at the model and device levels over time. While DL approaches achieve strong model-level accuracy, robust device-level identification remains challenging, particularly in modern imaging pipelines that involve camera-integrated or AI-driven enhancements during capture. These findings underscore the need for improved techniques and updated datasets to address evolving photograph capture practices.