---
tags:
- deepfake-detection
- computer-vision
- image-classification
- pytorch
- efficientnet
- swin-transformer
- security
library_name: pytorch
license: mit
metrics:
- accuracy
- f1
pipeline_tag: image-classification
inference: false
widgets:
  - text: "Test the DeepGuard Model Live"
    src: "https://harshasnade-deepfake-detection-system-v1.hf.space"
---

# DeepGuard - Deepfake Detection System

[![Live Demo](https://img.shields.io/badge/Demo-DeepGuard-blue?style=for-the-badge&logo=vercel)](https://deepfakescan.vercel.app/)
[![GitHub Repo](https://img.shields.io/badge/GitHub-Repository-black?style=for-the-badge&logo=github)](https://github.com/Harshvardhan-Asnade/Deepfake-Model)

## Model Details

### Model Description

DeepGuard is a robust Deepfake Detection System designed to identify AI-generated images with high precision. It employs an ensemble architecture combining **EfficientNetV2-S** and **Swin Transformer V2-T** with a custom Convolutional Neural Network (CNN) head. This hybrid approach leverages both local feature extraction (CNN) and global context understanding (Transformers) to spot manipulation artifacts often invisible to the human eye.

- **Developed by:** Harshvardhan Asnade
- **Model type:** Ensemble (EfficientNetV2 + SwinV2 + Custom CNN)
- **Language(s):** Python, PyTorch
- **License:** MIT
- **Finetuned from model:** Torchvision pre-trained weights (ImageNet)

### Model Sources

- **Repository:** https://github.com/Harshvardhan-Asnade/Deepfake-Model
- **Demo:** https://deepfakescan.vercel.app/ (Live Web App)

## Uses

### Direct Use

The model is designed to classify single images as either **REAL** or **FAKE**. It outputs a probability score (0.0 - 1.0) and a confidence metric. It is suitable for:
- Content moderation
- Social media verification
- Digital forensics (preliminary analysis)

### Out-of-Scope Use

- **Video Analysis:** While it can analyze individual frames, it does not currently leverage temporal coherence in videos (frame-by-frame analysis only).
- **Audio Deepfakes:** This model is strictly for visual content.
- **Legal Proof:** The model provides a probabilistic assessment and should not be the sole basis for legal judgments.

## How to Get Started with the Model

```python
import torch
import torch.nn as nn
from torchvision import models
from safetensors.torch import load_file
import cv2

# Define Model Architecture
class DeepfakeDetector(nn.Module):
    def __init__(self, pretrained=False):
        super(DeepfakeDetector, self).__init__()
        self.efficientnet = models.efficientnet_v2_s(weights='DEFAULT' if pretrained else None)
        self.swin = models.swin_v2_t(weights='DEFAULT' if pretrained else None)
        
        self.efficientnet.classifier = nn.Identity()
        self.swin.head = nn.Identity()
        
        self.classifier = nn.Sequential(
            nn.Linear(1280 + 768, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Dropout(0.4),
            nn.Linear(512, 1)
        )

    def forward(self, x):
        f1 = self.efficientnet(x)
        f2 = self.swin(x)
        combined = torch.cat((f1, f2), dim=1)
        return self.classifier(combined)

# Load Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DeepfakeDetector(pretrained=False).to(device)
state_dict = load_file("best_model.safetensors")
model.load_state_dict(state_dict)
model.eval()
```

## Training Details

### Training Data

The model was trained on a diverse dataset comprising:
- **Real Images:** FFHQ, CelebA-HQ
- **Deepfake Images:** Generated using StyleGAN2, Diffusion Models, and FaceSwap techniques.
- **Data Augmentation:** extensive augmentation (compression, noise, blur) was applied to robustify the model against social media re-compression artifacts.

## Evaluation

### Results

The model achieves high accuracy on standard benchmarks:
- **Test Accuracy:** ~92-95% (on unseen test split)
- **Generalization:** Shows strong resilience to JPEG compression compared to standard CNNs.

## Citation

```bibtex
@misc{deepguard2024,
  author = {Asnade, Harshvardhan},
  title = {DeepGuard: Ensemble Deepfake Detection System},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Harshasnade/Deepfake_Detection_System_V1}}
}
```