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---
license: apache-2.0
datasets:
- prithivMLmods/OpenDeepfake-Preview
language:
- en
base_model:
- google/siglip2-base-patch16-512
pipeline_tag: image-classification
library_name: transformers
tags:
- deepfake
- detection
- SigLIP2
- art
- synthetic
---
# open-deepfake-detection
> open-deepfake-detection is a vision-language encoder model fine-tuned from `siglip2-base-patch16-512` for binary image classification. It is trained to detect whether an image is fake or real using the *OpenDeepfake-Preview* dataset. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
> \[!important]
Experimental Model
```py
Classification Report:
precision recall f1-score support
Fake 0.9718 0.9155 0.9428 10000
Real 0.9201 0.9734 0.9460 9999
accuracy 0.9444 19999
macro avg 0.9459 0.9444 0.9444 19999
weighted avg 0.9459 0.9444 0.9444 19999
```
---
## Label Space: 2 Classes
The model classifies an image as either:
```
Class 0: Fake
Class 1: Real
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/open-deepfake-detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Fake",
"1": "Real"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=2, label="Deepfake Detection"),
title="open-deepfake-detection",
description="Upload an image to detect whether it is AI-generated (Fake) or a real photograph (Real), using the OpenDeepfake-Preview dataset."
)
if __name__ == "__main__":
iface.launch()
```
---
## Demo Inference
> [!warning]
real
> [!warning]
fake
## Intended Use
`open-deepfake-detection` is designed for:
* **Deepfake Detection** – Identify AI-generated or manipulated images.
* **Content Moderation** – Flag synthetic or fake visual content.
* **Dataset Curation** – Remove synthetic samples from mixed datasets.
* **Visual Authenticity Verification** – Check the integrity of visual media.
* **Digital Forensics** – Support image source verification and traceability. |