buildborderless/CommunityForensics-DeepfakeDet-ViT

NOTICE: 7/22/2026

• Ungated again. Friendly reminder to all to please respect the MIT license. Attribution should be made to the original project (see bottom) as well as this repository. Other than that, you're free to use the model as you wish.

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.

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 Borderless.

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
• Paper: arXiv: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:

@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}, 
}