---
tags:
  - image-classification
  - computer-vision
  - deepfake-detection
  - fine-tuned
license: mit
datasets:
  - 140k-real-and-fake-faces
metrics:
  - accuracy
---

# 🎭 SDXL-Deepfake-Detector

**A high-performance deep learning model for binary classification of real versus synthetically generated (deepfake) human faces.**

Developed by **[Sadra Milani Moghaddam](https://sadramilani.ir/)**, this model is designed to detect faces generated by state-of-the-art synthesis models—including those based on SDXL and similar architectures—while maintaining strong generalization across diverse image sources.

## 🚀 Model Overview

**SDXL-Deepfake-Detector** is a fine-tuned image classification model built using transfer learning. It leverages a pre-trained vision backbone and is optimized specifically for distinguishing authentic human faces from AI-generated forgeries.

### Key Features

- **Task**: Binary image classification (Real = 0, Deepfake = 1)  
- **Training Dataset**: [140k Real and Fake Faces (Kaggle)](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces)  
- **Test Accuracy**: **91%** on an independent hold-out test set  
- **Hardware Used for Training**: NVIDIA RTX 3060 (12GB VRAM)  
- **License**: [MIT](https://opensource.org/licenses/MIT)  

This model is suitable for integration into media forensics pipelines, content moderation systems, or any application requiring reliable deepfake detection at the image level.

---

## 💻 Usage

You can easily load and run inference with this model using the Hugging Face `transformers` library.

### Installation

```bash
pip install transformers torch pillow
```
```python
import argparse
from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import torch

def main():
    parser = argparse.ArgumentParser(
        description="Classify an image as 'Real' or 'Deepfake' using the SDXL-Deepfake-Detector."
    )
    parser.add_argument("--image", type=str, required=True, help="Path to the input image file")
    args = parser.parse_args()

    model_name = "SADRACODING/SDXL-Deepfake-Detector"
    model = AutoModelForImageClassification.from_pretrained(model_name)
    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    model.eval()

    image = Image.open(args.image).convert("RGB")
    inputs = feature_extractor(images=image, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits
    predicted_class_idx = logits.argmax(-1).item()
    predicted_label = model.config.id2label[predicted_class_idx]

    print(f"Predicted class index: {predicted_class_idx}")
    print(f"Predicted label: {predicted_label}")

if __name__ == "__main__":
    main()
```

```bash
python predict.py --image path/to/face_image.jpg
```
## 📄 License

This model is released under the [MIT License](https://opensource.org/licenses/MIT), allowing for both commercial and non-commercial use with proper attribution.

## 🙌 Acknowledgements

- **Dataset**: [140k Real and Fake Faces](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces) by xhlulu  
- **Framework**: [Hugging Face Transformers](https://huggingface.co/docs/transformers)
- **Github**: [SDXL-Deepfake-Detector](https://github.com/SadraCoding/SDXL-Deepfake-Detector)
- **Developer**: [Sadra Milani Moghaddam](https://sadramilani.ir/)