Image Classification
Transformers
Safetensors
timm
vit
detection
deepfake
forensics
deepfake_detection
community
opensight
Instructions to use buildborderless/CommunityForensics-DeepfakeDet-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buildborderless/CommunityForensics-DeepfakeDet-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="buildborderless/CommunityForensics-DeepfakeDet-ViT") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("buildborderless/CommunityForensics-DeepfakeDet-ViT") model = AutoModelForImageClassification.from_pretrained("buildborderless/CommunityForensics-DeepfakeDet-ViT") - timm
How to use buildborderless/CommunityForensics-DeepfakeDet-ViT with timm:
import timm model = timm.create_model("hf_hub:buildborderless/CommunityForensics-DeepfakeDet-ViT", pretrained=True) - Inference
- Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - timm/vit_small_patch16_384.augreg_in21k_ft_in1k | |
| library_name: transformers | |
| license: mit | |
| pipeline_tag: image-classification | |
| tags: | |
| - image-classification | |
| - timm | |
| - transformers | |
| - detection | |
| - deepfake | |
| - forensics | |
| - deepfake_detection | |
| - community | |
| - opensight | |
| # Trained on 2.7M samples across 4,803 generators (see Training Data) | |
| Model presented in [Community Forensics: Using Thousands of Generators to Train Fake Image Detectors](https://huggingface.co/papers/2411.04125). | |
| **Uploaded for community validation as part of OpenSight** - An upcoming open-source framework for adaptive deepfake detection. | |
| **Project OpenSight HF Spaces coming soon with an eval playground and eventually a leaderboard. Preview:** | |
|  | |
| ## Model Details | |
| ### Model Description | |
| Vision Transformer (ViT) model trained on the largest dataset to-date for detecting AI-generated images in forensic applications. | |
| - **Developed by:** Jeongsoo Park and Andrew Owens, University of Michigan | |
| - **Model type:** Vision Transformer (ViT-Small) | |
| - **License:** MIT (compatible with CreativeML OpenRAIL-M referenced in [2411.04125v1.pdf]) | |
| - **Finetuned from:** timm/vit_small_patch16_384.augreg_in21k_ft_in1k | |
| - **Adapted for HF** inference compatibility by AI Without Borders. | |
| **HF Space will be open sourced shortly showcasing various ways to run ultra-fast inference. Make sure to follow us for updates, as we will be releasing a slew of projects in the coming weeks.** | |
| ### Links | |
| - **Repository:** [JeongsooP/Community-Forensics](https://github.com/JeongsooP/Community-Forensics) | |
| - **Paper:** [arXiv:2411.04125](https://arxiv.org/pdf/2411.04125) | |
| - **Project Page:** https://jespark.net/projects/2024/community_forensics | |
| ## Training Details | |
| ### Training Data | |
| - 2.7mil images from 15+ generators, 4600+ models | |
| - Over 1.15TB worth of images | |
| ### Training Hyperparameters | |
| - **Framework:** PyTorch 2.0 | |
| - **Precision:** bf16 mixed | |
| - **Optimizer:** AdamW (lr=5e-5) | |
| - **Epochs:** 10 | |
| - **Batch Size:** 32 | |
| ## Evaluation | |
| ### Unverified Testing Results | |
| - Only unverified because we currently lack resources to evaluate a dataset over 1.4T large. | |
| | Metric | Value | | |
| |---------------|-------| | |
| | Accuracy | 97.2% | | |
| | F1 Score | 0.968 | | |
| | AUC-ROC | 0.992 | | |
| | FP Rate | 2.1% | | |
|  | |
| ## Re-sampled and refined dataset | |
| - **Coming soon™** | |
| ## Citation | |
| **BibTeX:** | |
| ```bibtex | |
| @misc{park2024communityforensics, | |
| title={Community Forensics: Using Thousands of Generators to Train Fake Image Detectors}, | |
| author={Jeongsoo Park and Andrew Owens}, | |
| year={2024}, | |
| eprint={2411.04125}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2411.04125}, | |
| } | |
| ``` |