ReviewSensors (Basel, Switzerland)2025
Recent Advances in Deep Learning-Based Source Camera Identification and Device Linking.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
2 authors.
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.