Model Card for Virtus

Virtus is a fine-tuned Vision Transformer (ViT) model for binary image classification, specifically trained to distinguish between real and deepfake images. It achieves ~99.2% accuracy on a balanced dataset of over 190,000 images.

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
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

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

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.

How to Get Started with the Model

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])

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

Epochs: 2
Learning rate: 1e-6
Train batch size: 32
Eval batch size: 8
Weight decay: 0.02
Optimizer: AdamW (via Trainer API)
Mixed precision: Not used

Evaluation

Testing Data

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

Metrics

Accuracy
F1 Score (macro)
Confusion matrix
Classification report

Results

Accuracy: 99.20%
F1 Score (macro): 0.9920

Environmental Impact

Hardware Type: NVIDIA Tesla V100 (Kaggle Notebook GPU)
Hours used: ~2.3 hours
Cloud Provider: Kaggle
Compute Region: Unknown
Carbon Emitted: Can be estimated via MLCO2 Calculator

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 or open an issue on the model repository. or mail me at [email protected]

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