| --- |
| license: mit |
| tags: |
| - deepfake-detection |
| - ai-generated-image-detection |
| - synthetic-media |
| - image-forensics |
| - computer-vision |
| - clip |
| - siglip |
| base_model: |
| - openai/clip-vit-large-patch14 |
| - google/siglip2-base-patch16-512 |
| pipeline_tag: image-classification |
| --- |
| |
|  |
|
|
| # DeepReality: Layer 2 detector weights |
|
|
| Trained weights for the Layer 2 detection core of **DeepReality**, a six-layer |
| forensic system for detecting AI-generated and manipulated imagery. |
|
|
| **Source code, documentation and the full pipeline:** |
| https://github.com/OmerKurtulus/DeepReality |
|
|
| These files are the three detectors that were trained for the project. They are |
| not a standalone classifier: DeepReality reads them as three of fifteen |
| independent analysis modules, and its verdict is produced by a reasoning layer |
| that weighs them against documentary provenance, compression physics and |
| localisation evidence. Used alone, they carry the limitations described below. |
|
|
| ## Files |
|
|
| | File | Pin | Size | Architecture | |
| |---|---|---|---| |
| | `pin_b1_clip_ln_tune_final.pt` | PIN-B1 | 1.6 GB | CLIP ViT-L/14, frozen backbone with LayerNorm tuning | |
| | `pin_b2_siglip2_finetune_final.pt` | PIN-B2 | 1.4 GB | SigLIP2-base-patch16-512, full fine-tune | |
| | `pin_b3_freq_cnn_final.pt` | PIN-B3 | 19 MB | Frequency CNN over DCT/DWT maps, trained from scratch | |
|
|
| Two further models complete the detection core and are **not** hosted here. |
| PIN-B4 is a public third-party checkpoint |
| ([prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2](https://huggingface.co/prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2)), |
| and the PIN-F1 XGBoost meta-learner is small enough to be tracked directly in |
| the GitHub repository. |
|
|
| ## Usage |
|
|
| Download the three files into the `models/` directory of a DeepReality clone: |
|
|
| ```bash |
| git clone https://github.com/OmerKurtulus/DeepReality.git |
| cd DeepReality |
| |
| hf download OmerKurtulus/deepreality-models --local-dir models |
| ``` |
|
|
| PIN-B1 and PIN-B2 additionally fetch their base architectures from the Hub on |
| first run. Placement instructions and the remaining setup are in |
| [`models/README.md`](https://github.com/OmerKurtulus/DeepReality/blob/main/models/README.md). |
|
|
| ## The system these weights belong to |
|
|
| A single image taken through all fifteen pins. Every figure is the output of one |
| real pipeline run, not an illustration. PIN-B1, PIN-B2 and PIN-B3 are the three |
| detectors hosted in this repository; the verdict is not theirs to make. |
|
|
|  |
|
|
| Note what happens in that run. The four detectors agree at a mean of 0.932, and |
| the reasoning layer still decides on the C2PA manifest instead, lowers its own |
| confidence because the signing certificate had expired, and flags that three of |
| the four detectors share a training corpus, so their agreement is correlated |
| rather than independent. |
|
|
| ## Training data |
|
|
| All three detectors were trained on **OpenDeepfake-Preview**, 20,000 images. |
|
|
| ## Reported performance |
|
|
| Held-out results on the training corpus: |
|
|
| | Pin | Accuracy | ROC-AUC | |
| |---|---|---| |
| | PIN-B1 | 99.77% | 0.9997 | |
| | PIN-B2 | 99.97% | 1.0000 | |
| | PIN-B3 | 96.50% | 0.9923 | |
|
|
| ## Limitations, and why they matter |
|
|
| **Read the figures above as in-distribution performance. They do not survive a |
| change of corpus.** Measured on |
| [`ComplexDataLab/OpenFake`](https://huggingface.co/datasets/ComplexDataLab/OpenFake) |
| the same detectors fall to 0.51 to 0.84 ROC-AUC. On |
| [`Hemg/deepfake-and-real-images`](https://huggingface.co/datasets/Hemg/deepfake-and-real-images) |
| all four sit at chance, and PIN-B3 is anti-correlated with the label, which is |
| worse than noise because a downstream fusion stage learns to trust it backwards. |
|
|
| This generalisation gap is the reason DeepReality exists. A single detector |
| reporting 99% on its own benchmark tells you very little about an unseen |
| generator, which is why the system combines paradigms that fail differently and |
| subordinates all of them to documentary provenance when provenance is present. |
|
|
| The only figure measured by cross-validation on a corpus none of these detectors |
| saw in training is the Layer 6 meta-learner: ROC-AUC 0.8846 at an Expected |
| Calibration Error of 0.0203. That is the conservative number, and the one to |
| quote. |
|
|
| **Intended use.** Research and assistive review. These weights should not be |
| used as the sole basis for a consequential decision about a specific image or |
| person. |
|
|
| ## Licence and citation |
|
|
| Released under the MIT licence. If you use this work, please cite it: |
|
|
| ```bibtex |
| @software{kurtulus2026deepreality, |
| author = {Kurtulus, Omer Faruk}, |
| title = {DeepReality: PIN Architecture for Multi-Layer AI Image Forensics}, |
| year = {2026}, |
| url = {https://github.com/OmerKurtulus/DeepReality}, |
| license = {MIT} |
| } |
| ``` |
|
|
