Efficient DeepFake Image Classification Using Lightweight MobileNetV4-Small Architecture

Savita Sidnal, Shradha Kekare, Soujanya Menasagi, Vaishnavi Tharakar, Uday Kulkarni, Shashank Hegde

2025

Abstract

The rise of Deep Learning (DL) has unlocked a wide range of transformative applications, including the creation of hyper-realistic synthetic images through Generative Adversarial Networks (GANs). While these images demonstrate the immense potential of DL, they also pose significant risks, such as misuse in cybersecurity breaches, political manipulation, and disinformation campaigns. This paper proposes a robust approach for deepfake detection using MobileNetV4-Small, a lightweight and efficient DL model. Leveraging advanced preprocessing techniques, the proposed method enhances the ability to distinguish counterfeit images from authentic ones. The study utilized a dataset containing real and fake images, achieving a notable test accuracy of 89.78%.The model’s performance was further analyzed through visual evaluation of classification results. This work underscores the efficacy of lightweight architectures in addressing the challenges posed by deep-fake media and provides a comparative analysis with existing approaches. Future enhancements could involve ensemble techniques and expanded datasets to further improve accuracy and generalization. The results affirm the critical role of DL models in mitigating the risks associated with synthetic media.

Paper Citation

in Harvard Style

Sidnal S., Kekare S., Menasagi S., Tharakar V., Kulkarni U. and Hegde S. (2025). Efficient DeepFake Image Classification Using Lightweight MobileNetV4-Small Architecture. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 247-253. DOI: 10.5220/0013613400004664

in Bibtex Style

@conference{incoft25,
author={Savita Sidnal and Shradha Kekare and Soujanya Menasagi and Vaishnavi Tharakar and Uday Kulkarni and Shashank Hegde},
title={Efficient DeepFake Image Classification Using Lightweight MobileNetV4-Small Architecture},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={247-253},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013613400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Efficient DeepFake Image Classification Using Lightweight MobileNetV4-Small Architecture
SN - 978-989-758-763-4
AU - Sidnal S.
AU - Kekare S.
AU - Menasagi S.
AU - Tharakar V.
AU - Kulkarni U.
AU - Hegde S.
PY - 2025
SP - 247
EP - 253
DO - 10.5220/0013613400004664
PB - SciTePress