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---
license: apache-2.0
pipeline_tag: image-classification
library_name: transformers
tags:
- deep-fake
- detection
- Image
- SigLIP2
base_model:
- google/siglip2-base-patch16-512
datasets:
- prithivMLmods/OpenDeepfake-Preview
language:
- en
---
# deepfake-detector-model-v1
> `deepfake-detector-model-v1` is a vision-language encoder model fine-tuned from google/siglip-base-patch16-512 for binary deepfake image classification. It is trained to detect whether an image is real or generated using synthetic media techniques. The model uses the `SiglipForImageClassification` architecture.
> [!warning]
Experimental
```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 one of the following:
```
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/deepfake-detector-model-v1"
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 Classification"),
title="deepfake-detector-model",
description="Upload an image to classify whether it is real or fake using a deepfake detection model."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`deepfake-detector-model` is designed for:
* **Deepfake Detection** – Accurately identify fake images generated by AI.
* **Media Authentication** – Verify the authenticity of digital visual content.
* **Content Moderation** – Assist in filtering synthetic media in online platforms.
* **Forensic Analysis** – Support digital forensics by detecting manipulated visual data.
* **Security Applications** – Integrate into surveillance systems for authenticity verification. |