Feature based classification of computer graphics and real images
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
Photorealistic images can now be created using advanced techniques in computer graphics (CG). Synthesized elements could easily be mistaken for photographic (real) images. Therefore we need to differentiate between CG and real images. In our work, we propose and develop a new framework based on an aggregate of existing features. Our framework has a classification accuracy of 90% when tested on the de facto standard Columbia dataset, which is 4% better than the best results obtained by other prominent methods in this area. We further show that using feature selection it is possible to reduce the feature dimension of our framework from 557 to 80 without a significant loss in performance (Lt 1%). We also investigate different approaches that attackers can use to fool the classification system, including creation of hybrid images and histogram manipulations. We then propose and develop filters to effectively detect such attacks, thereby limiting the effect of such attacks to our classification system.
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 | 0.000 |
| 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