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Electronics
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Deep Learning Approach for Secure and Trustworthy Biometric System
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Deep Learning Approach for Secure and Trustworthy Biometric System

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 18585

Special Issue Editors

Dr. Zitong Yu

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, Singapore
Interests: face anti-spoofing; rPPG; biometrics; AI security; video understanding; computer vision; machine learning
Dr. Yunxiao Qin

E-Mail Website
Co-Guest Editor
Neuroscience and Intelligent Media Institute, Communication University of China, Beijing 101101, China
Interests: meta-learning; adversarial attack; robustness; face anti-spoofing; continual learning
Dr. Changsheng Chen

E-Mail Website
Co-Guest Editor
College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
Interests: image forensics; biometrics; computer vision; machine learning
Dr. Zhaoqiang Xia

E-Mail Website
Co-Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710060, China
Interests: visual computing; image search; image recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Zuheng Ming

E-Mail Website
Co-Guest Editor
Laboratory of Information Processing and Transmission (L2TI), Institut Galilée, Université Sorbonne Paris Nord, 93430 Villetaneuse, France
Interests: computer vision; vision & language; biometrics; deep learning

Special Issue Information

Dear Colleagues, 

Biometrics has been widely used in many personal and enterprise application systems, such as facial payments and video surveillance. Biologically unique identifiers, such as the face, fingerprints, iris, palms and veins, gait, voice, physiological signals, etc., appear reliable. However, the biometric system encounters various security challenges. In particular, recently emerging techniques have enabled realistic digital attacks with manipulation tools (e.g., Deepfake), high-fidelity physical presentation attacks (e.g., print, replay, 3D mask, and makeup), and adversarial attacks with imperceptible perturbations to humans. The growing prevalence of misinformation related to such falsified personally identifiable information has heightened interest in secure and trustworthy biometric systems for the AI community.

Topics of interest include but are not limited to:

  • Attack detection for a wide range of biometrics (not limited to face, fingerprint, iris, palm print, gait, voice, biosignals, or remote photoplethysmography (rPPG));
  • Novel deep learning approaches for face spoofing, forgery, and morphing detection;
  • Adversarial attacks and backdoor attacks, as well as their defenses in biometrics;
  • Deep learning for document liveness and recapturing detection;
  • Analysis of robustness, generalization, and interpretability in biometric systems;
  • Learning with fewer labels in biometric systems;
  • Open-world biometric systems under unseen domains and unknown attacks;
  • Privacy-preserving based deep learning for biometric systems;
  • Review, survey, and new datasets on unimodal and multi-modal biometric systems.

Dr. Zitong Yu
Dr. Yunxiao Qin
Dr. Changsheng Chen
Dr. Zhaoqiang Xia
Dr. Zuheng Ming
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Electronics 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

  • face anti-spoofing
  • deepfake detection
  • face forgery detection
  • adversarial attack
  • robustness
  • biometrics
  • security and privacy
  • computer vision
  • deep learning

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Published Papers (5 papers)

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Research

20 pages, 25584 KiB  
Article
LIDeepDet: Deepfake Detection via Image Decomposition and Advanced Lighting Information Analysis
by Zhimao Lai, Jicheng Li, Chuntao Wang, Jianhua Wu and Donghua Jiang
Electronics 2024, 13(22), 4466; https://doi.org/10.3390/electronics13224466 - 14 Nov 2024
Viewed by 1544
Abstract
The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods [...] Read more.
The proliferation of AI-generated content (AIGC) has empowered non-experts to create highly realistic Deepfake images and videos using user-friendly software, posing significant challenges to the legal system, particularly in criminal investigations, court proceedings, and accident analyses. The absence of reliable Deepfake verification methods threatens the integrity of legal processes. In response, researchers have explored deep forgery detection, proposing various forensic techniques. However, the swift evolution of deep forgery creation and the limited generalizability of current detection methods impede practical application. We introduce a new deep forgery detection method that utilizes image decomposition and lighting inconsistency. By exploiting inherent discrepancies in imaging environments between genuine and fabricated images, this method extracts robust lighting cues and mitigates disturbances from environmental factors, revealing deeper-level alterations. A crucial element is the lighting information feature extractor, designed according to color constancy principles, to identify inconsistencies in lighting conditions. To address lighting variations, we employ a face material feature extractor using Pattern of Local Gravitational Force (PLGF), which selectively processes image patterns with defined convolutional masks to isolate and focus on reflectance coefficients, rich in textural details essential for forgery detection. Utilizing the Lambertian lighting model, we generate lighting direction vectors across frames to provide temporal context for detection. This framework processes RGB images, face reflectance maps, lighting features, and lighting direction vectors as multi-channel inputs, applying a cross-attention mechanism at the feature level to enhance detection accuracy and adaptability. Experimental results show that our proposed method performs exceptionally well and is widely applicable across multiple datasets, underscoring its importance in advancing deep forgery detection. Full article
(This article belongs to the Special Issue Deep Learning Approach for Secure and Trustworthy Biometric System)
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14 pages, 7087 KiB  
Article
Generated or Not Generated (GNG): The Importance of Background in the Detection of Fake Images
by Marco Tanfoni, Elia Giuseppe Ceroni, Sara Marziali, Niccolò Pancino, Marco Maggini and Monica Bianchini
Electronics 2024, 13(16), 3161; https://doi.org/10.3390/electronics13163161 - 10 Aug 2024
Cited by 2 | Viewed by 1419
Abstract
Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, [...] Read more.
Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision. Full article
(This article belongs to the Special Issue Deep Learning Approach for Secure and Trustworthy Biometric System)
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15 pages, 3909 KiB  
Article
Improving Remote Photoplethysmography Performance through Deep-Learning-Based Real-Time Skin Segmentation Network
by Kunyoung Lee, Jaemu Oh, Hojoon You and Eui Chul Lee
Electronics 2023, 12(17), 3729; https://doi.org/10.3390/electronics12173729 - 4 Sep 2023
Cited by 2 | Viewed by 2314
Abstract
In recent years, health-monitoring systems have become increasingly important in the medical and safety fields, including patient and driver monitoring. Remote photoplethysmography is an approach that captures blood flow changes due to cardiac activity by utilizing a camera to measure transmitted or reflected [...] Read more.
In recent years, health-monitoring systems have become increasingly important in the medical and safety fields, including patient and driver monitoring. Remote photoplethysmography is an approach that captures blood flow changes due to cardiac activity by utilizing a camera to measure transmitted or reflected light through the skin, but it has limitations in its sensitivity to changes in illumination and motion. Moreover, remote photoplethysmography signals measured from nonskin regions are unreliable, leading to inaccurate remote photoplethysmography estimation. In this study, we propose Skin-SegNet, a network that minimizes noise factors and improves pulse signal quality through precise skin segmentation. Skin-SegNet separates skin pixels and nonskin pixels, as well as accessories such as glasses and hair, through training on facial structural elements and skin textures. Additionally, Skin-SegNet reduces model parameters using an information blocking decoder and spatial squeeze module, achieving a fast inference time of 15 ms on an Intel i9 CPU. For verification, we evaluated Skin-SegNet using the PURE dataset, which consists of heart rate measurements from various environments. When compared to other skin segmentation methods with similar inference speeds, Skin-SegNet demonstrated a mean absolute percentage error of 1.18%, showing an improvement of approximately 60% compared to the 4.48% error rate of the other methods. The result even exhibits better performance, with only 0.019 million parameters, in comparison to DeepLabV3+, which has 5.22 million model parameters. Consequently, Skin-SegNet is expected to be employed as an effective preprocessing technique for facilitating efficient remote photoplethysmography on low-spec computing devices. Full article
(This article belongs to the Special Issue Deep Learning Approach for Secure and Trustworthy Biometric System)
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30 pages, 12091 KiB  
Article
Coordination Control of a Hybrid AC/DC Smart Microgrid with Online Fault Detection, Diagnostics, and Localization Using Artificial Neural Networks
by Ali M. Jasim, Basil H. Jasim, Bogdan-Constantin Neagu and Bilal Naji Alhasnawi
Electronics 2023, 12(1), 187; https://doi.org/10.3390/electronics12010187 - 30 Dec 2022
Cited by 24 | Viewed by 3194
Abstract
In this paper, a solar and wind renewable energies-based hybrid AC/DC microgrid (MG) is proposed for minimizing the number of DC/AC/DC power conversion processes. High penetration rates of renewable energy increase MG instability. This instability can be mitigated by maintaining a balance between [...] Read more.
In this paper, a solar and wind renewable energies-based hybrid AC/DC microgrid (MG) is proposed for minimizing the number of DC/AC/DC power conversion processes. High penetration rates of renewable energy increase MG instability. This instability can be mitigated by maintaining a balance between consumption demand and production levels. Coordination control is proposed in this study to address coordinated electricity flowing through both AC and DC links and to achieve system stability under variability of generation, load, and fault conditions. The MG adopts a bidirectional main converter that is controlled using a digital proportional resonant (PR) current controller in a synchronous reference frame. The PR controller plays a role as a digital filter with infinite impulse response (IIR) characteristics by virtue of its high gain at the resonant frequency, thereby reducing harmonics. Moreover, the applied PR controller quickly follows the reference signal, can adapt to changes in grid frequency, is easy to set up, and has no steady-state error. Moreover, the solar photovoltaic (PV)-based distribution generation (DG) is supported by a maximum power point tracker (MPPT)-setup boost converter to extract maximum power. Due to the usage of converter-connected DG units in MGs, power electronic converters may experience excessive current during short circuit faults. Fault detection is critical for MG control and operation since it empowers the system to quickly isolate and recover from faults. This paper proposed an intelligent online fault detection, diagnostic, and localization information system for hybrid low voltage AC/DC MGs using an artificial neural network (ANN) due to its accuracy, robustness, and quickness. The proposed scheme enables rapid detection of faults on the AC bus, resulting in a more reliable MG. To ensure the neural network’s validity, it was trained on various short circuit faults. The performance of the MG was evaluated using MATLAB software. The simulation findings indicate that the suggested control strategy maintains the dynamic stability of the MG, meets the load demand, and achieves energy balance as well as properly predicts faults. Full article
(This article belongs to the Special Issue Deep Learning Approach for Secure and Trustworthy Biometric System)
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25 pages, 4696 KiB  
Article
An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System
by Joseph Bamidele Awotunde, Rasheed Gbenga Jimoh, Agbotiname Lucky Imoize, Akeem Tayo Abdulrazaq, Chun-Ta Li and Cheng-Chi Lee
Electronics 2023, 12(1), 87; https://doi.org/10.3390/electronics12010087 - 26 Dec 2022
Cited by 20 | Viewed by 8527
Abstract
The privacy of individuals and entire countries is currently threatened by the widespread use of face-swapping DeepFake models, which result in a sizable number of fake videos that seem extraordinarily genuine. Because DeepFake production tools have advanced so much and since so many [...] Read more.
The privacy of individuals and entire countries is currently threatened by the widespread use of face-swapping DeepFake models, which result in a sizable number of fake videos that seem extraordinarily genuine. Because DeepFake production tools have advanced so much and since so many researchers and businesses are interested in testing their limits, fake media is spreading like wildfire over the internet. Therefore, this study proposes five-layered convolutional neural networks (CNNs) for a DeepFake detection and classification model. The CNN enhanced with ReLU is used to extract features from these faces once the model has extracted the face region from video frames. To guarantee model accuracy while maintaining a suitable weight, a CNN enabled with ReLU model was used for the DeepFake-detection-influenced video. The performance evaluation of the proposed model was tested using Face2Face, and first-order motion DeepFake datasets. Experimental results revealed that the proposed model has an average prediction rate of 98% for DeepFake videos and 95% for Face2Face videos under actual network diffusion circumstances. When compared with systems such as Meso4, MesoInception4, Xception, EfficientNet-B0, and VGG16 which utilizes the convolutional neural network, the suggested model produced the best results with an accuracy rate of 86%. Full article
(This article belongs to the Special Issue Deep Learning Approach for Secure and Trustworthy Biometric System)
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