Learning to localize image forgery using boundary‐preserving mask R‐ <scp>CNN</scp>
Bibliographic record
Abstract
Digital image manipulation is a growing concern in multimedia security. Many existing forgery detection methods struggle with accurately localizing manipulated regions-especially near boundaries-and often fail to generalize across different manipulation types. To address these challenges, we propose a novel Boundary-Preserving Mask R-CNN framework that enhances detection precision by incorporating boundary-aware features. The model integrates channel attention mechanisms to better capture detailed spatial information and leverages frequency domain features to improve robustness. We evaluated the framework on six diverse benchmark datasets-CASIA V2, Columbia, Carvalho, CoMoFoD, MICC-F220, and CG-1050-covering splicing, copy-move, and compositing manipulations. Extensive preprocessing ensured uniform input, and pixel-level segmentation enabled accurate region detection. Our method demonstrated strong performance across multiple metrics, including accuracy, precision, recall, F1-score, IoU, and AUC. These results highlight its potential as a reliable tool for digital forensics and investigative applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".