| --- |
| 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. |
|
|
