ReviewSensors (Basel, Switzerland)2025
Algorithms and Methods for Individual Source Camera Identification: A Survey.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Interpol review of forensic image and video analysis, 2022-2025.Forensic science international. Synergy · 2026Review
- Recent Advances in Deep Learning-Based Source Camera Identification and Device Linking.Sensors (Basel, Switzerland) · 2025Review
- A Review of Neural Network-Based Image Noise Processing Methods.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Source camera identification (SCI) is a key issue in the field of digital forensics. This paper presents a comprehensive review of the existing methods and algorithms used for this purpose. It discusses approaches based on matrix noise analysis, including methods utilizing sensor pattern noise, photo response non-uniformity, statistical methods, aberrations analysis, as well as modern techniques based on deep neural networks and machine learning. Particular attention is paid to the effectiveness and robustness of the algorithms to different types of interference and their possible application in practical cases, such as law enforcement investigations. Moreover, we also discuss the issue of camera identification using videos and provide a brief description of popular image datasets that might be used for source camera identification method benchmarking.
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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.