Real Images Hold the Key to Detecting AI-Generated Images Using Bayer Pattern Analysis
Bibliographic record
Abstract
With the increasing availability of AI-based image generators, it has become remarkably easy to create realistic synthesized images. While this technological progress enables many creative and practical applications, it also raises serious concerns over misuse, such as the spread of misinformation and fraudulent activities. As a result, reliable detection of synthesized images has become critical. Existing detection methods often rely on supervised learning using labeled datasets that include both authentic and synthesized images, which limits their generalization to unseen AI generators. In this paper, we propose a novel one-class detection method that leverages only real images during training. Our approach is based on exploiting the Bayer pattern, a characteristic signature of digital camera sensors. We incorporate a Variational Autoencoder (VAE) to effectively extract statistical differences in pixel variances introduced by the demosaicing process. Furthermore, we design our method to be invariant to different Bayer pattern variants. Experimental evaluations showed that our approach outperforms other state-of-the-art methods in detecting authentic and synthesized images.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".