faisalishfaq2005/deepfake-detection-efficientnet-vit

Deepfake Detection with Improved EfficientViT

Data Preprocessing

In this study, we utilized the free version of FaceForen- sics++ dataset and the Celeb-DF dataset available on Kaggle. From these datasets, we selected a total of 1,580 videos, with an equal distribution of 790 real and 790 fake videos. To prepare the data for training, we first calculated the duration of each video to ensure consistent frame extraction. We then extracted 20 evenly spaced frames from each video, allowing us to capture representative visual information while avoiding redundancy. For each extracted frame, we employed the MTCNN (Multi-task Cascaded Convolutional Networks) to detect and crop the faces. MTCNN is a popular face detection algorithm that efficiently localizes facial regions while handling variations in pose and lighting. Each detected face was resized to a uniform size of 224x224 pixels to maintain consistency across the dataset. This preprocessing pipeline helped in standardizing the input data, making it suitable for training the deep learning model. The cropped face images were saved for subsequent training. During training, we applied data augmentation techniques to increase the diversity and robustness of the model. These augmentations simulate real-world variations, helping the model generalize better. The augmentations included random resized cropping, horizontal flipping, rotation, color jittering, Gaussian blurring, affine transformations, grayscale conversion, and normalization. These transformations mimic different visual conditions, thereby reducing the risk of over- fitting.

Model Architecture and Methodology

Deep learning models have shown remarkable performance in the field of image analysis and classification. CNNs have traditionally been the go-to architecture due to their ability to capture localized spatial features effectively. How- ever, when it comes to understanding complex patterns and long-range dependencies in images, ViTs have emerged as a more powerful alternative. In this study, we leverage the strengths of both architectures by combining EfficientNet- B0 for spatial feature extraction with a ViT for relational modeling. This hybrid approach aims to efficiently capture both fine-grained and global patterns inherent in deepfake videos. The core of our model architecture is a hybrid design that integrates EfficientNet-B0 and a Vision Transformer (ViT). EfficientNet-B0 acts as the feature extractor, providing spatial representations, while the ViT component captures long-range dependencies through self-attention. The input to the model is a batch of face images. These images are first processed through EfficientNet-B0, which extracts spatial features in the form of 1,280 feature maps, each of size 7x7, resulting in a tensor of shape [batch, 1280, 7, 7]. To make these features suitable for the transformer encoder, we flatten the spatial dimensions, transforming the tensor to [batch, 49, 1280], where 49 represents the number of tokens derived from the flattened feature maps. This structure can be understood by drawing a parallel to natural language processing (NLP), where each token represents a word. Here, each of the 49 tokens corresponds to a spatial patch of the image. Next, we reduce the dimensionality of each feature vector from 1,280 to 384 using a linear layer. This results in a tensor of shape [batch, 49, 384]. A learnable CLS (classification) token is appended to the beginning of the sequence, forming a tensor of shape [batch, 50, 384]. This token aggregates information from the entire image and is ultimately used for classification. Positional embeddings are then added to retain spatial information since transformers inherently lack the ability to capture positional relationships. The resulting tensor is passed through six consecutive ViT blocks. Each block comprises multi-head attention (MHA) and feed-forward neural network layers. The MHA mechanism allows the model to focus on various parts of the image simultaneously, capturing complex interdependencies between facial features. Residual connections are incorporated within each ViT block to enhance gradient flow, preventing the vanishing gradient problem. Dropout layers are added at various stages to reduce overfitting and improve generalization.

The final representation from the CLS token is passed through an MLP head comprising linear and GELU layers to output the probability of the input being a deepfake. This combination of EfficientNet for spatial encoding and ViT for relational modeling leads to a robust architecture capable of capturing both local and global patterns.

The loss function used for training is Binary Cross En- tropy with Logits (BCEWithLogitsLoss), which is well- suited for binary classification tasks as it combines a sigmoid layer with binary cross-entropy loss. The optimizer chosen is AdamW, known for its effective weight decay, with a learning rate of 1e-4 and weight decay of 1e-4. To further optimize training, we use a learning rate scheduler that reduces the learning rate by a factor of 0.5 when the validation loss plateaus for three epochs, promoting efficient convergence.

Inference Pipeline

Comparison of model performance with and without Transformer on the FaceForensics++ and Caleb DF datasets

Performance of the proposed model at different batch sizes

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}
}