Virtus

🚀 Virtus

Virtus is a fine - tuned Vision Transformer (ViT) model tailored for binary image classification. It's specifically designed to differentiate between real and deepfake images, achieving an accuracy of approximately 99.2% on a balanced dataset of over 190,000 images.

🚀 Quick Start

from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
import torch

model = AutoModelForImageClassification.from_pretrained("agasta/virtus")
extractor = AutoFeatureExtractor.from_pretrained("agasta/virtus")

image = Image.open("path_to_image.jpg")
inputs = extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()
print(model.config.id2label[predicted_class])

✨ Features

• High Accuracy: Achieves ~99.2% accuracy on a balanced dataset of over 190,000 images.
• Binary Classification: Specifically trained for distinguishing between real and deepfake images.
• Versatile Use: Can be deployed in image analysis pipelines or integrated into applications for media authenticity detection.

📦 Installation

No specific installation steps are provided in the original README.

📚 Documentation

Model Details

Model Description

Virtus is based on facebook/deit - base - distilled - patch16 - 224 and was fine - tuned on a binary classification task using a large dataset of real and fake facial images. The training process involved class balancing, data augmentation, and evaluation using accuracy and F1 score.

• Developed by: [Agasta](https://github.com/Itz - Agasta)
• Funded by: None
• Shared by: Agasta
• Model type: Vision Transformer (ViT) for image classification
• Language(s): N/A (vision model)
• License: MIT
• Finetuned from model: [facebook/deit - base - distilled - patch16 - 224](https://huggingface.co/facebook/deit - base - distilled - patch16 - 224)

Model Sources

• Repository: https://huggingface.co/agasta/virtus

Uses

Direct Use

This model can be used to predict whether an input image is a real or a deepfake. It can be deployed in image analysis pipelines or integrated into applications that require media authenticity detection.

Downstream Use

Virtus may be used in broader deepfake detection systems, educational tools for detecting synthetic media, or pre - screening systems for online platforms.

Out - of - Scope Use

• Detection of deepfakes in videos or audio
• General object classification tasks outside of the real/fake binary domain

Bias, Risks, and Limitations

The dataset, while balanced, may still carry biases in facial features, lighting conditions, or demographics. The model is also not robust to non - standard input sizes or heavily occluded faces.

Recommendations

💡 Usage Tip

• Use only on face images similar in nature to the training set.
• Do not use for critical or high - stakes decisions without human verification.
• Regularly re - evaluate performance with updated data.

Training Details

Training Data

The dataset consisted of 190,335 self - collected real and deepfake face images, with RandomOverSampler used to balance the two classes. The data was split into 60% training and 40% testing, maintaining class stratification.

Training Procedure

Preprocessing

• Images resized to 224x224
• Augmentations: Random rotation, sharpness adjustments, normalization

Training Hyperparameters

Evaluation

Testing Data

Same dataset, stratified 60:40 split, used for evaluation.

Metrics

• Accuracy
• F1 Score (macro)
• Confusion matrix
• Classification report

Results

Environmental Impact

Technical Specifications

Model Architecture and Objective

The model is a distilled Vision Transformer (DeiT) designed for image classification with a binary objective: classify images as Real or Fake.

Compute Infrastructure

• Hardware: 1x NVIDIA Tesla V100 GPU
• Software: PyTorch, Hugging Face Transformers, Datasets, Accelerate

Citation

BibTeX:

@misc{virtus2025,
  title={Virtus: Deepfake Detection using Vision Transformers},
  author={Agasta},
  year={2025},
  howpublished={\url{https://huggingface.co/agasta/virtus}},
}

APA: Agasta. (2025). Virtus: Deepfake Detection using Vision Transformers. Hugging Face. https://huggingface.co/agasta/virtus

Model Card Contact

For questions or feedback, reach out via [GitHub](https://github.com/Itz - Agasta) or open an issue on the [model repository](https://github.com/Itz - Agasta/Lopt/tree/main/models/image). or mail me at rupam.golui@proton.me

📄 License

This project is licensed under the MIT license.