---
license: mit
tags:
- deepfake-detection
- computer-vision
- transfer-learning
- hybrid-cnn
- pytorch
datasets:
- xhlulu/140k-real-and-fake-faces
metrics:
- accuracy
---

# Deepfake Face Detector

A hybrid CNN model for detecting AI-generated (deepfake) face images, built using transfer learning on a dual-stream architecture.

## Model Architecture

This model combines two parallel CNN streams:

- **Spatial Stream** — EfficientNet-B4 pretrained on ImageNet, fine-tuned to detect visible artifacts such as unnatural skin texture, blending edges, and facial inconsistencies
- **Frequency Stream** — Xception backbone with fixed SRM (Steganalysis Rich Model) filters to detect invisible GAN noise fingerprints in pixel residuals
- **Fusion Module** — Both stream outputs are concatenated (1792 + 2048 = 3840 features) and passed through a classification head with Dropout regularization
- **Output** — Sigmoid activation producing a probability score (0 = fake, 1 = real)

## Training Strategy

Three-phase transfer learning approach:

| Phase | Layers Trained | Learning Rate | Epochs |
|-------|---------------|---------------|--------|
| 1 | Classifier head only | 1e-3 | 3 |
| 2 | Top EfficientNet blocks + classifier | 1e-4 | 3 |

## Dataset

Trained and evaluated on [140k Real and Fake Faces](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces)

| Split | Real | Fake | Total |
|-------|------|------|-------|
| Train | 50,000 | 50,000 | 100,000 |
| Valid | 10,000 | 10,000 | 20,000 |
| Test  | 10,000 | 10,000 | 20,000 |

Fake images are StyleGAN2-generated faces. Real images are sourced from Flickr-Faces-HQ (FFHQ).

## Performance

| Metric | Score |
|--------|-------|
| Test Accuracy | 98.59% |
| Validation Accuracy | 98.55% |

## Usage
```python
import torch
from torchvision import transforms
from PIL import Image

model = HybridDeepfakeDetector()
model.load_state_dict(torch.load("deepfake_detector_phase2.pth", map_location="cpu"))
model.eval()

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

image = Image.open("face.jpg").convert("RGB")
tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    prob = model(tensor).item()

label = "REAL" if prob > 0.5 else "FAKE"
print(f"{label} ({prob*100:.1f}% confidence)")
```

## Limitations

- Optimized for StyleGAN2 generated faces — may perform differently on other GAN architectures
- Best results on frontal face images
- Not tested on video deepfakes

## Try It

Live demo available at [Hugging Face Spaces](https://huggingface.co/spaces/AdityaManojShinde/deepfake-detector-app)