MorphDet: Towards the Detection of Morphing Attacks

Jival Kapoor, Priyanka Singh, Manoranjan Mohanty

2025

Abstract

Biometric authentication systems have become an inevitable part of the society. They are based on the primary traits of an individual that are unique and hard to forge or manipulate by simple means. However, the unprecedented growth of technology has enabled the access of so many advanced tools that could be used for forging these traits. In this paper, we focus on the face morphing attacks. A basic pipeline is used to generate morphed attacks. A face morph detection model based on Resnet-152 is proposed and validated through exhaustive experiments. A dataset of 28,890 images is also contributed to conduct the experiments for varied scenarios, including simple face images, faces with beards, faces with eyeglasses, and a combination of beard and eyeglasses. Comparative performance analysis is done with the other state-of-the-art models i.e. Alexnet and VGG-16 and the proposed framework is found to outperform them.

Paper Citation

in Harvard Style

Kapoor J., Singh P. and Mohanty M. (2025). MorphDet: Towards the Detection of Morphing Attacks. In Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-760-3, SciTePress, pages 729-734. DOI: 10.5220/0013623200003979

in Bibtex Style

@conference{secrypt25,
author={Jival Kapoor and Priyanka Singh and Manoranjan Mohanty},
title={MorphDet: Towards the Detection of Morphing Attacks},
booktitle={Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2025},
pages={729-734},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013623200003979},
isbn={978-989-758-760-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - MorphDet: Towards the Detection of Morphing Attacks
SN - 978-989-758-760-3
AU - Kapoor J.
AU - Singh P.
AU - Mohanty M.
PY - 2025
SP - 729
EP - 734
DO - 10.5220/0013623200003979
PB - SciTePress