README.md

---
license: other
license_name: mixed-upstream-licenses
license_link: https://github.com/mever-team/sidbench/blob/main/LICENSE
pipeline_tag: image-classification
tags:
  - synthetic-image-detection
  - ai-generated-image-detection
  - deepfake-detection
  - image-forensics
  - sidbench
  - benchmark
  - pytorch
---

# SIDBench — Pretrained Detector Weights

Pretrained weights for [SIDBench](https://github.com/mever-team/sidbench).

**31 checkpoints, 8.74 GB.**

## Download

From the root of a SIDBench checkout:

```bash
pip install -U "huggingface_hub[cli]"
hf download dkarageo/sidbench --local-dir weights
```

This yields `./weights/<method>/<checkpoint>`, matching the default `--ckpt` paths in
`options/options.py` and `models/models.py`.

If you would like to run only a subset of the models, download just what they need, with
one `--include` per pattern:

```bash
# UnivFD only (4.1 kB)
hf download dkarageo/sidbench --local-dir weights --include "univfd/*"

# Rine + PatchCraft + NPR (~129 MB)
hf download dkarageo/sidbench --local-dir weights \
    --include "rine/*" --include "rptc/*" --include "npr/*"
```

`Dire`, `LGrad` and `DeFake` each need an additional file from `preprocessing/` or
`defake/`, listed under Auxiliary networks below.

## Contents

### Detector checkpoints

Each is passed via `--ckpt`, together with the matching `--modelName`.

| File | Size (MB = 10⁶ B) | `--modelName` | Trained on | md5 |
|---|---|---|---|---|
| `cnndetect/blur_jpg_prob0.1.pth` | 282.4 MB | `CNNDetect` | proGAN, augmented (recompressed) with 10% probability | `109a7a7406f52a3f658dcd982b58f333` |
| `cnndetect/blur_jpg_prob0.5.pth` | 282.4 MB | `CNNDetect` | proGAN, augmented (recompressed) with 50% probability | `0c0bd6f572eaec0e8ea8ed8b033969dd` |
| `dimd/corvi22_latent_model.pth` | 282.5 MB | `DIMD` | Latent Diffusion | `56fc46cd42550fe1b4ed819be26e2bfd` |
| `dimd/corvi22_progan_model.pth` | 282.5 MB | `DIMD` | proGAN | `a1163e6633acc6f7aac81dbbafa6d3b0` |
| `dimd/gandetection_resnet50nodown_progan.pth` | 282.4 MB | `DIMD` | proGAN | `f56d78a7092453e3b3b55bd285c38027` |
| `dimd/gandetection_resnet50nodown_stylegan2.pth` | 282.5 MB | `DIMD` | styleGAN2 | `a18f559b83ecac595e1588eea29999ad` |
| `dire/lsun_adm.pth` | 282.5 MB | `Dire` | ADM (diffusion) | `02bfd4b29c97e15e82777c474dc3c81f` |
| `dire/lsun_iddpm.pth` | 282.5 MB | `Dire` | IDDPM | `41c57afccc6a13bddd4b1c99366577e4` |
| `dire/lsun_pndm.pth` | 282.5 MB | `Dire` | PNDM | `7841ebc388f5690c400b9c48aaf71e32` |
| `dire/lsun_stylegan.pth` | 282.5 MB | `Dire` | styleGAN | `eaf1839b23a0b3ab308ad7e8d4e4956e` |
| `freqdetect/DCTAnalysis.pth` | 94.4 MB | `FreqDetect` | — | `02c3d38cad027db02baf3564722ae6f4` |
| `fusing/PSM.pth` | 297.5 MB | `Fusing` | — | `64a67251abf0c501cbf53b1436b5ec7e` |
| `gramnet/Gram.pth` | 47.1 MB | `GramNet` | — | `71e8d0aeb1030c959506cf3e45736a67` |
| `lgrad/LGrad.pth` | 282.6 MB | `LGrad` | proGAN | `69fdb9f9f8ad10ad33c183a56151e01c` |
| `lgrad/LGrad-1class-Trainon-Progan_horse.pth` | 94.4 MB | `LGrad` | proGAN, one class | `16f34fa71ac44b574c8dea1f0bb3179f` |
| `lgrad/LGrad-2class-Trainon-Progan_chair_horse.pth` | 94.4 MB | `LGrad` | proGAN, two classes | `918f9a66bc0141c73d4e21f389532cdc` |
| `lgrad/LGrad-4class-Trainon-Progan_car_cat_chair_horse.pth` | 94.4 MB | `LGrad` | proGAN, four classes | `e40903550e6530a35d6b2f74b8d076f4` |
| `npr/NPR.pth` | 17.4 MB | `NPR` | — | `35d0f34154358af6b38157154b443f53` |
| `rine/model_1class_trainable.pth` | 42.1 MB | `Rine` | proGAN, one class | `ef625cfaf25ee6c4a77c70064fda3443` |
| `rine/model_2class_trainable.pth` | 1.1 MB | `Rine` | proGAN, two classes | `2959eb954e4afa1e2a6222a8ad6f7bf3` |
| `rine/model_4class_trainable.pth` | 25.3 MB | `Rine` | proGAN, four classes | `8931ede3fa2f6f6e98df0dd1563fa946` |
| `rine/model_ldm_trainable.pth` | 42.1 MB | `Rine` | Latent Diffusion, one class | `ce0e5a4ea018b49511ec1f9972b8487a` |
| `rptc/RPTC.pth` | 512.7 kB | `PatchCraft` | proGAN | `271ec9c97551ab2ce19e1d8bb6545059` |
| `univfd/fc_weights.pth` | 4.1 kB | `UnivFD` | proGAN | `392b2d8b637e932a2534ded56d9185bd` |
| `defake/clip_linear.pth` | 2.6 MB | `DeFake` | hybrid image+text detector, diffusion images | `bba621d9877a5a795bdb1a0670d0ae5e` |

`Rine` parses `model_<ncls>_trainable` out of the filename to select its architecture; do
not rename those four files.

### Auxiliary networks

| File | Size (MB = 10⁶ B) | Consumed by | md5 |
|---|---|---|---|
| `freqdetect/dct_mean.zip` | 1.2 MB | `--dctMean` (FreqDetect) | `19daa38673b9e2e7c2125e3c81080738` |
| `freqdetect/dct_var.zip` | 1.2 MB | `--dctVar` (FreqDetect) | `dc31d4f90068d15a5d79cce470955941` |
| `preprocessing/karras2019stylegan-bedrooms-256x256_discriminator.pth` | 92.3 MB | `--LGradGenerativeModelPath` (LGrad) | `12b5b30f3386cb09692757224e124795` |
| `preprocessing/lsun_bedroom.pt` | 2.21 GB | `--DireGenerativeModelPath` (Dire) | `34d5da60938c66eca1f327d17f54acfa` |
| `defake/finetune_clip.pt` | 353.7 MB | `--defakeClipEncoderPath` (DeFake) | `3853db6a3282e60b08d5559a3cef2e2d` |
| `defake/model_base_capfilt_large.pth` | 2.12 GB | `--defakeBlipPath` (DeFake) | `dd40ed17486a858be6b2e085caba57a8` |


## Provenance

| Files | Method | Paper | Original code |
|---|---|---|---|
| `cnndetect/` | CNNDetect | CNN-generated images are surprisingly easy to spot...for now | [peterwang512/CNNDetection](https://github.com/peterwang512/CNNDetection) |
| `dimd/` | DIMD | On the detection of synthetic images generated by diffusion models | [grip-unina/DMimageDetection](https://github.com/grip-unina/DMimageDetection) |
| `freqdetect/` | FreqDetect | Leveraging Frequency Analysis for Deep Fake Image Recognition | [RUB-SysSec/GANDCTAnalysis](https://github.com/RUB-SysSec/GANDCTAnalysis) |
| `fusing/` | Fusing | Fusing global and local features for generalized AI-synthesized image detection | [littlejuyan/FusingGlobalandLocal](https://github.com/littlejuyan/FusingGlobalandLocal) |
| `gramnet/` | GramNet | Global Texture Enhancement for Fake Face Detection In the Wild | [liuzhengzhe/Global\_Texture\_Enhancement...](https://github.com/liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild) |
| `lgrad/`, `preprocessing/karras*` | LGrad | Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection | [chuangchuangtan/LGrad](https://github.com/chuangchuangtan/LGrad) |
| `dire/`, `preprocessing/lsun_bedroom.pt` | Dire | DIRE for Diffusion-Generated Image Detection | [ZhendongWang6/DIRE](https://github.com/ZhendongWang6/DIRE) |
| `univfd/` | UnivFD | Towards Universal Fake Image Detectors that Generalize Across Generative Models | [Yuheng-Li/UniversalFakeDetect](https://github.com/Yuheng-Li/UniversalFakeDetect) |
| `npr/` | NPR | Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection | [chuangchuangtan/NPR-DeepfakeDetection](https://github.com/chuangchuangtan/NPR-DeepfakeDetection) |
| `rptc/` | PatchCraft | PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection | [project page](https://fdmas.github.io/AIGCDetect/) |
| `defake/` | DeFake | DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models | [zeyangsha/De-Fake](https://github.com/zeyangsha/De-Fake) |
| `rine/` | Rine | Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection | [mever-team/rine](https://github.com/mever-team/rine) |

## Integrity

Every checkpoint was loaded into the SIDBench model class that consumes it, with PyTorch
2.3.1, before upload. md5 sums are listed above.

## License

SIDBench is [Apache 2.0](https://github.com/mever-team/sidbench/blob/main/LICENSE). The
weights were produced by the authors of the individual methods and remain subject to the
licence terms of the projects listed under Provenance.

## Citation

```bibtex
@inproceedings{schinas2024sidbench,
  title     = {SIDBench: A Python framework for reliably assessing synthetic image detection methods},
  author    = {Schinas, Manos and Papadopoulos, Symeon},
  booktitle = {Proceedings of the 3rd ACM International Workshop on Multimedia AI against Disinformation (MAD '24)},
  year      = {2024},
  doi       = {10.1145/3643491.3660277},
  eprint    = {2404.18552},
  archivePrefix = {arXiv}
}
```

Cite also the original paper of any individual detector used.