Natural image splicing detection based on defocus blur at edges
Why this work is in the frame
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Bibliographic record
Abstract
Defocus blur has been used as a cue in image splicing detection. At present, existing methods mainly rely on consistency checking of defocus kernels estimated along suspicious edges (and other reference edges if applicable). However, the texture, nearby edges, light fields as well as noises will influence the information of defocus blur at the natural edges in a certain range, resulting in inconsistent edge defocus blur estimation. As a result, it makes the splicing detection unreliable. In this paper, we analyze the feature of the defocus blur on both the spliced edges and the natural edges, and propose a novel difference-of-defocus-blur based natural image splicing detection method. Compared to the state-of-the-art methods, the proposed method can detect splicing more robustly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it