README.md

---
license: mit
tags:
  - deepfake-detection
  - image-classification
  - efficientnet
  - pytorch
  - computer-vision
  - timm
metrics:
  - accuracy
pipeline_tag: image-classification
---

# DeepShield — EfficientNet-B4 Deepfake Detector

A fine-tuned **EfficientNet-B4** model for binary deepfake detection, trained on 140,000 real and AI-generated face images. Achieves ~99% validation accuracy.

> 🔴 Live demo: [Forensa — Deepfake Detection App](https://huggingface.co/spaces/Yashikaysn29/forensa)

---

## Model Details

| Property | Value |
|----------|-------|
| **Architecture** | EfficientNet-B4 (via `timm`) + custom classification head |
| **Task** | Binary image classification (Real vs Fake) |
| **Input** | RGB image, resized to 224×224 |
| **Output** | Probability score (0 = Fake, 1 = Real) |
| **Validation Accuracy** | ~99% |
| **Model Size** | 72.8 MB |
| **Training Hardware** | Google Colab (Tesla T4 GPU) |

---

## Architecture

```python
import torch.nn as nn
import timm

class DeepfakeDetector(nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone = timm.create_model('efficientnet_b4', pretrained=False, num_classes=0)
        self.classifier = nn.Sequential(
            nn.Linear(self.backbone.num_features, 256),
            nn.ReLU(),
            nn.Dropout(0.4),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.classifier(self.backbone(x))
```

---

## Training

- **Dataset**: 140,000 real and AI-generated/deepfaked face images (Kaggle)
- **Class balance**: 50% real, 50% fake
- **Preprocessing**: Resize to 224×224, ImageNet normalization
- **Optimizer**: Adam
- **Loss**: Binary Cross-Entropy
- **Epochs**: Trained until convergence on validation set
- **Augmentation**: Random horizontal flip, rotation, color jitter

---

## Usage

```python
import torch
import timm
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from huggingface_hub import hf_hub_download

# Model definition
class DeepfakeDetector(nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone = timm.create_model('efficientnet_b4', pretrained=False, num_classes=0)
        self.classifier = nn.Sequential(
            nn.Linear(self.backbone.num_features, 256),
            nn.ReLU(),
            nn.Dropout(0.4),
            nn.Linear(256, 1),
            nn.Sigmoid()
        )
    def forward(self, x):
        return self.classifier(self.backbone(x))

# Load model
model_path = hf_hub_download(repo_id="Yashikaysn29/deepshield", filename="best_model.pth")
model = DeepfakeDetector()
model.load_state_dict(torch.load(model_path, map_location='cpu'))
model.eval()

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

# Inference
def predict(image_path):
    img = Image.open(image_path).convert("RGB")
    tensor = transform(img).unsqueeze(0)
    with torch.no_grad():
        prob = model(tensor).item()
    label = "REAL" if prob >= 0.5 else "FAKE"
    confidence = prob * 100 if prob >= 0.5 else (1 - prob) * 100
    return label, round(confidence, 2)

label, confidence = predict("your_image.jpg")
print(f"{label} — {confidence}% confidence")
```

---

## Performance

| Metric | Value |
|--------|-------|
| Validation Accuracy | ~99% |
| Task | Binary Classification |
| Threshold | 0.5 (score ≥ 0.5 = REAL) |

---

## Limitations

- Optimized for **face images** — performance may degrade on non-face content
- May not generalize to all deepfake generation techniques, especially newer methods
- Not intended for use as a sole evidence source in legal or forensic contexts

---

## Live Demo

Try the model live via the **Forensa** web app:
👉 [huggingface.co/spaces/Yashikaysn29/forensa](https://huggingface.co/spaces/Yashikaysn29/forensa)

Supports image and video input with confidence scoring and session analytics.

---

## About

Built by **Yashika Saxena** — B.Tech AI & ML, Institute of Technology and Management, Gwalior (2023–2027).

- GitHub: [github.com/Yashikaysn](https://github.com/Yashikaysn)
- HF Space: [Forensa](https://huggingface.co/spaces/Yashikaysn29/forensa)