A Rotation-Invariant Convolutional Neural Network for Image Enhancement Forensics
Bibliographic record
Abstract
Many proposed complex convolutional neural network (CNN) models in image forensics are with a large number of parameters, requiring a huge number of training data and having the risk of being overfitting. Considering the desired rotation invariance in the detection of some specific image manipulations, i.e., image enhancement, we propose employing convolutional filters with an isotropic architecture in the CNN model which can significantly reduce the required number of CNN parameters. With the same weights in symmetric positions, the proposed filter can extract rotation-invariant features for image enhancement forensics. Experimental results show that the proposed rotation-invariant CNN models with much less parameters can achieve much better performance, e.g., yielding more than 13% improvement in terms of detection accuracy in Gamma correction forensics. It also achieves significantly better generalization performances on different databases and better robustness against JPEG compression when compared with the popular BayarNet in [16].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".