EfficientNet Combined with Generative Adversarial Networks for Presentation Attack Detection

被引:1
作者
Sandouka, Soha B. [1 ]
Bazi, Yakoub [1 ]
Al Rahhal, Mohamad Mahmoud [2 ]
机构
[1] King Saud Univ, Comp Engn Dept, Riyadh, Saudi Arabia
[2] King Saud Univ, Appl Comp Sci Dept, Riyadh, Saudi Arabia
来源
2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE & MODERN ASSISTIVE TECHNOLOGY (ICAIMAT) | 2020年
关键词
Fingerprint presentation attack detection; liveness detection; deep learning; Convolutional Neural Networks (CNN); generalization; Generative Adversarial Network (GANs);
D O I
10.1109/icaimat51101.2020.9308017
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
in recent years, fingerprint-based biometric systems have grown rapidly as they are used for various applications such as mobile payments, international border security, and financial transactions. Although the widespread of these systems, it has been found that they are vulnerable to presentation attacks (i.e., spoof attacks). Therefore, improving the generalization ability of fingerprint PAD over unknown materials and unknown sensors is of primary importance. In this work, we proposed a fingerprint PAD with improved cross-sensor and cross-material generalization based on state-of-the-art CNN network; i.e., EfficientNet combined with Generative Adversarial Network (GANs). We will validate the proposed methodologies on the public LivDet2015 dataset provided by the liveness detection competition.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Plant Biopotential Sensing Based on Generative Adversarial Networks for Environmental Anomaly Detection
    Zhao, Hanqing
    Nambo, Hidetaka
    [J]. IEEE SENSORS JOURNAL, 2023, 23 (23) : 29793 - 29803
  • [42] Future of generative adversarial networks (GAN) for anomaly detection in network security: A review
    Lim, Willone
    Yong, Kelvin Sheng Chek
    Lau, Bee Theng
    Tan, Colin Choon Lin
    [J]. COMPUTERS & SECURITY, 2024, 139
  • [43] DeepCGAN: early Alzheimer's detection with deep convolutional generative adversarial networks
    Ali, Imad
    Saleem, Nasir
    Alhussein, Musaed
    Zohra, Benazeer
    Aurangzeb, Khursheed
    Haq, Qazi Mazhar ul
    [J]. FRONTIERS IN MEDICINE, 2024, 11
  • [44] A Performance Evaluation of Defect Detection by using Denoising AutoEncoder Generative Adversarial Networks
    Komoto, Kyosuke
    Nakatsuka, Shunsuke
    Aizawa, Hiroaki
    Kato, Kunihito
    Kobayashi, Hiroyuki
    Banno, Kazumi
    [J]. 2018 INTERNATIONAL WORKSHOP ON ADVANCED IMAGE TECHNOLOGY (IWAIT), 2018,
  • [45] Using Generative Adversarial Networks and Transfer Learning for Breast Cancer Detection by Convolutional Neural Networks
    Guan, Shuyue
    Loew, Murray
    [J]. MEDICAL IMAGING 2019: IMAGING INFORMATICS FOR HEALTHCARE, RESEARCH, AND APPLICATIONS, 2019, 10954
  • [46] Vehicle license plate detection and recognition using deep neural networks and generative adversarial networks
    Zhang, Xiaoci
    Gu, Naijie
    Ye, Hong
    Lin, Chuanwen
    [J]. JOURNAL OF ELECTRONIC IMAGING, 2018, 27 (04)
  • [47] Towards Optimizing Malware Detection: An Approach Based on Generative Adversarial Networks and Transformers
    Alzahem, Ayyub
    Boulila, Wadii
    Driss, Maha
    Koubaa, Anis
    Almomani, Iman
    [J]. COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2022, 2022, 13501 : 598 - 610
  • [48] GAPF: Curve Text Detection based on Generative Adversarial Networks and Pixel Fluctuations
    Yang, Jun
    Zhang, Zhaogong
    Wang, Xuexia
    [J]. VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP, 2021, : 545 - 552
  • [49] Blood Flow Anomaly Detection via Generative Adversarial Networks: A Preliminary Study
    Singanamalli, Asha
    Mitra, Jhimli
    Wallace, Kirk
    Venugopal, Prem
    Smith, L. Scott
    Mo, Larry
    Leung, Lai Yee
    Morrison, Jonathan
    Rasmussen, Todd
    Marinelli, Luca
    [J]. MEDICAL IMAGING 2020: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING, 2021, 11315
  • [50] Generative Adversarial Network Models for Anomaly Detection in Software-Defined Networks
    Zacaron, Alexandro Marcelo
    Lent, Daniel Matheus Brandao
    da Silva Ruffo, Vitor Gabriel
    Carvalho, Luiz Fernando
    Proenca Jr, Mario Lemes
    [J]. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 2024, 32 (04)