Article Per Year (5 Year)
p-Index From 2021 - 2026
2.56
P-Index
This Author published in this journals
All Journal PROtek : Jurnal Ilmiah Teknik Elektro Jurnal Eksplora Informatika ILKOM Jurnal Ilmiah International Journal of Environmental, Sustainability, and Social Science Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat DIPANEGARA KOMPUTER TEKNOLOGI INFORMATIKA (DIPAKOMTI) DIPANEGARA KOMPUTER SISTEM INFORMASI (DIPAKOMSI) Journal of Vocational, Informatics and Computer Education
Indo Intan
Department Of Informatics Engineering, Universitas Dipa Makassar, Makassar, Indonesia
Arts Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
An Adaptive Attention-Based Multimodal Deep Fusion Network for Robust Biometric User Authentication Nurdin; Indo Intan; St. Aminah Dinayati Gani; Nur Salman; Erni Marlina
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.448

Abstract

Purpose - The global rise in identity fraud and credential-based breaches, with average costs reaching USD 4.88 million, highlights the limitations of unimodal biometric authentication systems, which are vulnerable to spoofing attacks and environmental degradation. Existing multimodal approaches also rely on static weighting strategies that lack adaptability to adversarial conditions or degraded data quality.This study proposes a Multimodal Deep Fusion Network (MDFN), an end-to-end deep learning architecture that integrates three biometric modalities: facial (visual), voice (audio), and keystroke dynamics (behavioral). The MDFN employs three independent feature extraction streams—ResNet-18 for visual data, 1D CNN for audio data, and Bi-LSTM for behavioral data—fused through an Attention-based Adaptive Weighted Fusion mechanism that dynamically adjusts to data quality. Methods - The evaluation was conducted using the VGGFace2, VoxCeleb1, and CMU Keystroke datasets under both normal and spoofing attack scenarios, using metrics such as EER, FAR, FRR, AUC, and APCER. Findings - The results show that the MDFN achieves an EER of 1.12% and an FAR of 1.12%, significantly outperforming the unimodal baselines (best EER: 4.15%) and static fusion models (EER: 1.95%). The system also demonstrated strong robustness against spoofing, achieving an APCER as low as 0.8%. Research Implications - MDFN is an effective authentication solution for high-security environments. Originality - Its key contribution lies in the attention-based adaptive fusion mechanism, which dynamically adjusts the modality weights based on a real-time quality assessment.
Co-Authors Akhriana, Asmah Ardimansyah Ardimansyah Aswar TC. Koswara Azza Farakhiyah Diah Larasati Umar Jaya Erni Marlina Faizal Faizal, Faizal Fitriaty Pangerang H.Umar, St. Amina Haris, Adammas Herlinda Herlinda, Herlinda ICHWAN, Munirul Johan Tandean Karnyoto, Andrea Stevens Martina Pineng Mira, Amirah Mochtar Yunus, Farhan Muhammad Alamsyah nfn Nurlina Nur Salman Nurani Nurani Nurdiansah, Nurdiansah Nurdin Nurdin Nurdin Oktavianus, Michael PRASETYO, Arif Eko Rhadi Indrawan Rismayani Rismayani Salman Salman Sihotang, Fernando St. Aminah Dinayati Gani ST. Aminah Dinayati Ghani Suci Ramadhani Arifin Suryani Suryani SUSILO, Sofiyan Dwi Tamsir, Nurlindasari TRISTANTO, Andhika Dwipradipta Vivi Rosida Wahyu Prasetya, Wahyu Wiranto Arismunandar