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Applied Sciences
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Advances in Sensitive and Biometric Information Protection
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Advances in Sensitive and Biometric Information Protection

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2026 | Viewed by 5306

Special Issue Editors

Dr. Dario Allegra

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Interests: image processing; computer vision; pattern recognition
Dr. Georgia Fargetta

E-Mail Website
Guest Editor Assistant
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Interests: optimization; computer vision; adversarial machine learning

Special Issue Information

Dear Colleagues,

As smart devices are increasingly used in our daily lives, their importance in managing our sensitive personal data is also increasing. Regarding protecting privacy, deep learning, such as generative adversarial networks (GANs), deep neural networks (DNNs), temporal convolutional networks (TCNs), and convolutional neural networks (CNNs), stands out. These tools analyze data with high accuracy and are widely used. They detect, identify, analyze, classify, and extract features from comprehensive datasets including various smart devices and attack scenarios, achieve the accurate protection of various scenario datasets, promote secure data sharing for research and analysis, and protect personal privacy.

This Special Issue aims to study the latest progress and potential directions of deep learning in the field of sensitive information protection. Research papers and reviews in related fields are welcome. Research areas may include (but are not limited to) the following:

  • Deep learning.
  • Segmentation.
  • Feature extraction.
  • Generative adversarial networks.
  • Sensitive information.
  • Information security.
  • Convolutional neural networks.
  • Classification based on deep learning.
  • Biometric information.

We look forward to receiving your contributions.

Dr. Dario Allegra
Guest Editor

Dr. Georgia Fargetta
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • segmentation feature extraction
  • generative adversarial networks
  • sensitive information
  • information security
  • convolutional neural networks
  • classification based on deep learning
  • biometric information

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Published Papers (2 papers)

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31 pages, 2659 KB  
Article
ShieldNet: A Novel Adversarially Resilient Convolutional Neural Network for Robust Image Classification
by Arslan Manzoor, Georgia Fargetta, Alessandro Ortis and Sebastiano Battiato
Appl. Sci. 2026, 16(3), 1254; https://doi.org/10.3390/app16031254 - 26 Jan 2026
Viewed by 527
Abstract
The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike [...] Read more.
The proliferation of biometric authentication systems in critical security applications has highlighted the urgent need for robust defense mechanisms against sophisticated adversarial attacks. This paper presents ShieldNet, an adversarially resilient Convolutional Neural Network (CNN) framework specifically designed for secure iris biometric authentication. Unlike existing approaches that apply adversarial training or gradient regularization independently, ShieldNet introduces a synergistic dual-layer defense framework featuring three key components: (1) an attack-aware adaptive weighting mechanism that dynamically balances defense priorities across multiple attack types, (2) a smoothness-regularized gradient penalty formulation that maintains differentiable gradients while encouraging locally smooth loss landscapes, and (3) a consistency loss component that enforces prediction stability between clean and adversarial inputs. Through extensive experimental validation across three diverse iris datasets, MMU1, CASIA-Iris-Africa, and UBIRIS.v2, and rigorous evaluation against strong adaptive attacks including AutoAttack, PGD-100 with random restarts, and transfer-based black-box attacks, ShieldNet demonstrated robust performance, achieving 87.3% adversarial accuracy under AutoAttack on MMU1, 85.1% on CASIA-Iris-Africa, and 82.4% on UBIRIS.v2, while maintaining competitive clean data accuracies of 94.7%, 93.9%, and 92.8%, respectively. The proposed framework outperforms existing state-of-the-art defense methods including TRADES, MART, and AWP, achieving an equal error rate (EER) as low as 2.8% and demonstrating consistent robustness across both gradient-based and gradient-free attack scenarios. Comprehensive ablation studies validate the complementary contributions of each defense component, while latent space analysis confirms that ShieldNet learns genuinely robust feature representations rather than relying on gradient obfuscation. These results establish ShieldNet as a practical and reliable solution for deployment in high-security biometric authentication environments. Full article
(This article belongs to the Special Issue Advances in Sensitive and Biometric Information Protection)
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22 pages, 4399 KB  
Article
Deep Learning-Based Fingerprint–Vein Biometric Fusion: A Systematic Review with Empirical Evaluation
by Sarah Almuwayziri, Abeer Al-Nafjan, Hessah Aljumah and Mashael Aldayel
Appl. Sci. 2025, 15(15), 8502; https://doi.org/10.3390/app15158502 - 31 Jul 2025
Cited by 3 | Viewed by 4200
Abstract
User authentication is crucial for safeguarding access to digital systems and services. Biometric authentication serves as a strong and user-friendly alternative to conventional security methods such as passwords and PINs, which are often susceptible to breaches. This study proposes a deep learning-based multimodal [...] Read more.
User authentication is crucial for safeguarding access to digital systems and services. Biometric authentication serves as a strong and user-friendly alternative to conventional security methods such as passwords and PINs, which are often susceptible to breaches. This study proposes a deep learning-based multimodal biometric system that combines fingerprint (FP) and finger vein (FV) modalities to improve accuracy and security. The system explores three fusion strategies: feature-level fusion (combining feature vectors from each modality), score-level fusion (integrating prediction scores from each modality), and a hybrid approach that leverages both feature and score information. The implementation involved five pretrained convolutional neural network (CNN) models: two unimodal (FP-only and FV-only) and three multimodal models corresponding to each fusion strategy. The models were assessed using the NUPT-FPV dataset, which consists of 33,600 images collected from 140 subjects with a dual-mode acquisition device in varied environmental conditions. The results indicate that the hybrid-level fusion with a dominant score weight (0.7 score, 0.3 feature) achieved the highest accuracy (99.79%) and the lowest equal error rate (EER = 0.0018), demonstrating superior robustness. Overall, the results demonstrate that integrating deep learning with multimodal fusion is highly effective for advancing scalable and accurate biometric authentication solutions suitable for real-world deployments. Full article
(This article belongs to the Special Issue Advances in Sensitive and Biometric Information Protection)
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