metadata
license: mit
library_name: transformers
tags:
- pytorch
- image-classification
Deepfake Detection that Generalizes Across Benchmarks (WACV 2026)
This is the GenD (DINO) model from Tab. 2 in the paper.
Set up environment
conda create --name GenD python=3.12 uv
conda activate GenD
uv pip install -r requirements.txt
Usage
import requests
import torch
from PIL import Image
from src.hf.modeling_gend import GenD
model = GenD.from_pretrained("yermandy/GenD_DINOv3_L")
urls = [
"https://github.com/yermandy/deepfake-detection/blob/main/datasets/FF/DF/000_003/000.png?raw=true",
"https://github.com/yermandy/deepfake-detection/blob/main/datasets/FF/real/000/000.png?raw=true",
]
images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
tensors = torch.stack([model.feature_extractor.preprocess(img) for img in images])
logits = model(tensors)
probs = logits.softmax(dim=-1)
print(probs)