faisalishfaq2005/deepfake-detection-efficientnet-vit

Deepfake Detection with Improved EfficientViT

Model Architecture

Inference Pipeline

This repository contains a PyTorch model for deepfake detection based on an improved EfficientViT architecture, trained on video data.

The model predicts whether a video is real (0) or fake (1) using both visual information and temporal cues.

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🧩 Model Description

Architecture: Improved EfficientViT
Backbone: EfficientNet-B0 for feature extraction
Head: Transformer-based temporal modeling with classification head
Input: Video frames (224×224 RGB images)
Output: Binary label (0=Real, 1=Fake) and frame-level probabilities

Key Features:

• Extracts faces from frames using MTCNN
• Supports inference on raw video files
• Provides frame-level probabilities for fine-grained analysis

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📁 Repository Structure

deepfake-efficientvit/
│
├── model.py                  # ImprovedEfficientViT class
├── inference.py              # Functions to run inference on videos
├── model.pth  # Trained weights
├── config.json               # Optional model metadata
├── requirements.txt          # Required packages
├── README.md

⚡ Installation

git clone https://huggingface.co/faisalishfaq2005/deepfake-detection-efficientnet-vit

cd deepfake-detection-efficientnet-vit

pip install -r requirements.txt

🚀 Usage

1.Programmatic Inference

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import torch
from model import ImprovedEfficientViT
from inference import predict_vedio 

# 1️⃣ Download the checkpoint from Hugging Face
checkpoint_path = hf_hub_download(
    repo_id="faisalishfaq2005/deepfake-detection-efficientnet-vit",  
    filename="model.safetensors"
)

# 2️⃣ Load the model weights safely
state_dict = load_file(checkpoint_path, device="cpu")
model = ImprovedEfficientViT()
model.load_state_dict(state_dict)
model.eval()

# 4️⃣ Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# 3️⃣ Run inference on a video
video_path = "sample_video.mp4"
result = predict_vedio(video_path, model)
print(result)
# Example Output: {'class': 1}

2. Manual Download

Go to the Hugging Face model page

Download:

model.pth

model.py

inference.py

Place them in the same folder locally.

Install requirements and run predict_video().

📄 License

This model is released under the MIT License. You are free to use, modify, and distribute it, with attribution.

📚 Citation

If you use this model in your research, please cite:

@inproceedings{faisalishfaq2025efficientvit,
  title={Deepfake Detection with Efficientnet and ViT},
  author={Faisal Ishfaq},
  year={2025}
}