README.md

---
license: mit
tags:
- deepfake-detection
- computer-vision
- image-classification
- xceptionnet
- face-forensics
- pytorch
- synthetic-media
- media-forensics
metrics:
- accuracy
- auc
- f1
language:
- en
library_name: pytorch
pipeline_tag: image-classification
---

# FaceForge Detector: State-of-the-Art Deepfake Detection

[![Paper](https://img.shields.io/badge/Paper-Zenodo-blue)](https://doi.org/10.5281/zenodo.18530439)
[![GitHub](https://img.shields.io/badge/GitHub-Repository-black)](https://github.com/Huzaifanasir95/FaceForge)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

🎯 **99.33% Accuracy | 0.9995 AUC-ROC | 0.67% Error Rate**

## Model Description

FaceForge Detector is a high-performance deepfake detection model based on XceptionNet architecture that achieves state-of-the-art results on the FaceForensics++ dataset. This model can distinguish authentic faces from AI-generated deepfakes with exceptional accuracy.

**Key Features:**
- 🚀 99.33% accuracy on test set (1,500 samples)
- 📊 0.9995 AUC-ROC score
- ⚡ <200ms inference time per image
- 🎯 Only 2 false negatives out of 750 deepfakes (0.27% miss rate)
- 🔧 22M trainable parameters
- 💻 Trained efficiently on CPU (7.5 hours)

## Model Architecture

```
XceptionNet Backbone (20.8M params)
├── Entry Flow
├── Middle Flow (8 blocks)
├── Exit Flow
├── Global Average Pooling
└── Custom Classification Head (1.1M params)
    ├── Dropout (p=0.5)
    ├── FC(2048 → 512) + ReLU
    ├── Dropout (p=0.3)
    └── FC(512 → 2) [Real/Fake]
```

## Performance Metrics

### Test Set Results (1,500 samples)

| Metric | Value |
|--------|-------|
| **Accuracy** | 99.33% |
| **AUC-ROC** | 0.9995 |
| **Precision** | 98.94% |
| **Recall** | 99.73% |
| **F1 Score** | 0.9934 |
| **Specificity** | 98.93% |

### Confusion Matrix

|  | Predicted Real | Predicted Fake |
|--|----------------|----------------|
| **Actual Real** | 742 (TN) | 8 (FP) |
| **Actual Fake** | 2 (FN) | 748 (TP) |

**Total Errors:** 10 out of 1,500 (0.67%)

## Comparison with State-of-the-Art

| Method | Accuracy | AUC-ROC |
|--------|----------|---------|
| MesoNet | 83.1% | 0.847 |
| XceptionNet (base) | 95.3% | 0.969 |
| EfficientNet-B4 | 96.8% | 0.981 |
| Capsule Network | 97.4% | 0.986 |
| Face X-ray | 98.1% | 0.991 |
| **FaceForge (Ours)** | **99.33%** | **0.9995** |

## Usage

### Installation

```bash
pip install torch torchvision timm pillow numpy
```

### Inference

```python
import torch
import timm
from PIL import Image
from torchvision import transforms

# Load model
model = timm.create_model('xception', pretrained=False, num_classes=2)
checkpoint = torch.load('detector_best.pth', map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Preprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

# Inference
def detect_deepfake(image_path):
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform(image).unsqueeze(0)
    
    with torch.no_grad():
        logits = model(input_tensor)
        probs = torch.softmax(logits, dim=1)
        prediction = torch.argmax(probs, dim=1).item()
        confidence = probs[0][prediction].item()
    
    label = "REAL" if prediction == 0 else "FAKE"
    return label, confidence

# Example
label, confidence = detect_deepfake("face.jpg")
print(f"Prediction: {label} (Confidence: {confidence:.2%})")
```

### Gradio Demo

```python
import gradio as gr

def predict(image):
    label, confidence = detect_deepfake(image)
    return f"{label} ({confidence:.2%})"

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="filepath"),
    outputs="text",
    title="FaceForge Deepfake Detector",
    description="Upload a face image to detect if it's real or AI-generated"
)

demo.launch()
```

## Training Details

### Dataset
- **Source:** FaceForensics++ (c40 compression)
- **Training:** 7,000 images (3,500 real + 3,500 fake)
- **Validation:** 1,500 images (750 real + 750 fake)
- **Test:** 1,500 images (750 real + 750 fake)

### Hyperparameters
```yaml
optimizer: AdamW
learning_rate: 1e-4
weight_decay: 1e-4
batch_size: 32
epochs: 10
lr_schedule: Cosine Annealing (1e-4 → 1e-6)
augmentation:
  - Random Horizontal Flip (p=0.5)
  - Color Jitter (brightness=0.2, contrast=0.2, saturation=0.2)
```

### Training Time
- **Total:** 7.52 hours (451.5 minutes)
- **Per Epoch:** ~45 minutes
- **Hardware:** CPU (no GPU required)
- **Best Checkpoint:** Epoch 8 (0.9998 AUC-ROC)

## Limitations

1. **Dataset Scope:** Trained on FaceForensics++ deepfakes; may need fine-tuning for other manipulation methods
2. **Single Frame:** Processes individual images; doesn't leverage temporal information from videos
3. **Compression:** Trained on c40 compression; performance may vary with different quality levels
4. **Domain:** Optimized for face-centric images; may struggle with partial faces or unusual angles

## Ethical Considerations

This model is intended for:
✅ Research and education
✅ Content moderation
✅ Forensic analysis
✅ Fact-checking

**Not intended for:**
❌ Malicious surveillance
❌ Discriminatory profiling
❌ Invasion of privacy

## Citation

```bibtex
@techreport{nasir2026faceforge,
  title={FaceForge: A Deep Learning Framework for Facial Manipulation Generation and Detection},
  author={Nasir, Huzaifa},
  institution={National University of Computer and Emerging Sciences},
  year={2026},
  doi={10.5281/zenodo.18530439}
}
```

## Links

- 📄 **Paper:** https://doi.org/10.5281/zenodo.18530439
- 💻 **Code:** https://github.com/Huzaifanasir95/FaceForge
- 🎨 **Generator Model:** https://huggingface.co/Huzaifanasir95/faceforge-generator
- 🌐 **Interactive Demo:** See notebook `03_detector_and_adversarial.ipynb`

## License

This model is released under CC BY 4.0 license. See LICENSE file for details.

## Author

**Huzaifa Nasir**  
National University of Computer and Emerging Sciences (NUCES)  
Islamabad, Pakistan  
📧 nasirhuzaifa95@gmail.com

## Acknowledgments

- FaceForensics++ dataset creators
- PyTorch and timm library developers
- XceptionNet architecture (François Chollet)