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---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- deepfake
---
# **AI-vs-Deepfake-vs-Real-v2.0**
> **AI-vs-Deepfake-vs-Real-v2.0** is an image classification vision-language encoder model fine-tuned from `google/siglip2-base-patch16-224` for a single-label classification task. It is designed to distinguish AI-generated images, deepfake images, and real images using the `SiglipForImageClassification` architecture.
```py
"label2id": {
"Artificial": 0,
"Deepfake": 1,
"Real": 2
},
```
```py
"log_history": [
{
"epoch": 1.0,
"eval_accuracy": 0.9915991599159916,
"eval_loss": 0.0240725576877594,
"eval_model_preparation_time": 0.0023,
"eval_runtime": 248.0631,
"eval_samples_per_second": 40.308,
"eval_steps_per_second": 5.039,
"step": 313
}
```
The model categorizes images into three classes:
- **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models.
- **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered.
- **Class 2:** "Real" – The image is an authentic, unaltered photograph.
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/AI-vs-Deepfake-vs-Real-v2.0"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def image_classification(image):
"""Classifies an image as AI-generated, deepfake, or real."""
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()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=image_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Classification Result"),
title="AI vs Deepfake vs Real Image Classification",
description="Upload an image to determine whether it is AI-generated, a deepfake, or a real image."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use**
The **AI-vs-Deepfake-vs-Real-v2.0** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image.
### Potential Use Cases:
- **Deepfake Detection:** Identifying manipulated deepfake content in media.
- **AI-Generated Image Identification:** Distinguishing AI-generated images from real or deepfake images.
- **Content Verification:** Supporting fact-checking and digital forensics in assessing image authenticity.
- **Social Media and News Filtering:** Helping platforms flag AI-generated or deepfake content. |