Using the local information of image to identify the source camera
Bibliographic record
Abstract
In this paper we introduce a new method for source identification in digital image forensics. The proposed method uses local information of the inherent pattern of the camera, as a signature of the camera for source identification. Here the sensor pattern noise is used as the unique identification property of the camera. However due to content dependency of the denoising algorithms that are used to extract the noise pattern, the different regions of the image do not have the same information about the camera signature. Hence in our algorithm, at first the best regions of the image according to their local information are selected to extract the noise pattern. This step is done by fuzzy-based classification on the overlapped blocks of the image. In the next step the noise pattern of these regions are extracted and then, we evaluate the correlation between the image pattern and camera pattern. Finally the source camera is determined according its correlation. The experimental results compared to similar works show an increase in the detection rate of source identification, while computational complexity is reduced; this affirms the efficiency and performance of the proposed theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".