---
language: en
tags:
  - image-classification
  - ai-detection
  - vit
license: mit
---

# AI Image Detector

## Model Description
This model is designed to detect whether an image is real or AI-generated. It uses Vision Transformer (ViT) architecture to provide accurate classification.

## Model Usage
```python
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch

# تحميل النموذج والمعالج
processor = ViTImageProcessor.from_pretrained("C:/Users/SUPREME TECH/Desktop/SAM3/ai-image-detector")
model = ViTForImageClassification.from_pretrained("C:/Users/SUPREME TECH/Desktop/SAM3/ai-image-detector")

def detect_image(image_path):
    # فتح وتجهيز الصورة
    image = Image.open(image_path)
    if image.mode != 'RGB':
        image = image.convert('RGB')
    
    # معالجة الصورة
    inputs = processor(images=image, return_tensors="pt")
    
    # الحصول على التنبؤات
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = outputs.logits.softmax(dim=-1)
    
    # تحليل النتائج
    scores = predictions[0].tolist()
    results = [
        {"label": "REAL", "score": scores[0]},
        {"label": "FAKE", "score": scores[1]}
    ]
    
    # ترتيب النتائج حسب درجة الثقة
    results.sort(key=lambda x: x["score"], reverse=True)
    
    return {
        "prediction": results[0]["label"],
        "confidence": f"{results[0]['score']*100:.2f}%",
        "detailed_scores": [
            f"{r['label']}: {r['score']*100:.2f}%" 
            for r in results
        ]
    }

# كود للاختبار
if __name__ == "__main__":
    # يمكنك تغيير مسار الصورة هنا
    image_path = "path/to/your/image.jpg"
    
    try:
        result = detect_image(image_path)
        print("\nنتائج تحليل الصورة:")
        print(f"التصنيف: {result['prediction']}")
        print(f"درجة الثقة: {result['confidence']}")
        print("\nالتفاصيل:")
        for score in result['detailed_scores']:
            print(f"- {score}")
            
    except Exception as e:
        print(f"حدث خطأ: {str(e)}")
```

## Classes
The model classifies images into two categories:

- **Real Image (0)**: The image is real and not AI-generated.
- **AI Generated (1)**: The image is generated by AI.

## Technical Details
- **Model Architecture**: Vision Transformer (ViT)
- **Input**: Images (RGB)
- **Output**: Binary classification with confidence score
- **Max Image Size**: 224x224 (automatically resized)

## Requirements
- `transformers>=4.30.0`
- `torch>=2.0.0`
- `Pillow>=9.0.0`

## Limitations
- Best performance with clear, high-quality images.
- May have reduced accuracy with heavily edited photos.
- Designed for general image detection.

## Web Integration Example
```javascript
async function detectImage(imageFile) {
  const formData = new FormData();
  formData.append('image', imageFile);

  const response = await fetch('YOUR_API_ENDPOINT', {
    method: 'POST',
    body: formData
  });

  return await response.json();
}
```

## Developer
- **Created by**: yaya36095
- **License**: MIT
- **Repository**: [https://huggingface.co/yaya36095/ai-image-detector](https://huggingface.co/yaya36095/ai-image-detector)