![bXfKBT3LQkbeLzPCBHTGT.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Hav19ftsb_5u76rmEa7OL.png)

# **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.
artificial deepfake real