Modeling the EXIF-Image correlation for image manipulation detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
EXchangeable Image File format (EXIF) is a metadata header containing shot-related camera settings such as aperture, exposure time, ISO speed etc. These settings can affect the photo content in many ways. In this paper, we investigate the underlying EXIF-Image correlation and propose a novel model, which correlates image statistical noise features with several commonly used EXIF features. By formulating each EXIF feature as a weighted combination of different image statistical noise features, we first select a compact image statistical noise feature set using sequential floating forward selection. The underlying correlation as a set of regression weights is then solved using a least squares solution. When applying our learned correlation to detect image manipulation, we achieve average test accuracies of 94.6%, 94.1% and 94.9% in three different cameras to detect the presence of common image brightness and contrast adjustment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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