Protection against adversarial attacks with randomization of recognition algorithm

被引:0
|
作者
Marshalko, Grigory [1 ,2 ]
Koreshkova, Svetlana [3 ]
机构
[1] Tech Comm Standardisat Cryptog & Secur Mech TC 02, Moscow, Russia
[2] Higher Sch Econ, Moscow, Russia
[3] JSC Kryptonite, Moscow, Russia
关键词
Biometric recognition; Statistical distance; Local binary patterns; Password based authentication;
D O I
10.1007/s11416-023-00503-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We study a randomized variant of one type of biometric recognition algorithms, which is intended to mitigate adversarial attacks. We show that the problem of an estimation of the security of the proposed algorithm can be formulated in the form of an estimation of statistical distance between the probability distributions, induced by the initial and the randomized algorithm. A variant of practical password-based implementation is discussed. The results of experimental evaluation are given. The preliminary verison of this research was presented at CTCrypt 2020 workshop.
引用
收藏
页码:127 / 133
页数:7
相关论文
共 50 条
  • [1] Protection against adversarial attacks with randomization of recognition algorithm
    Grigory Marshalko
    Svetlana Koreshkova
    Journal of Computer Virology and Hacking Techniques, 2024, 20 (1) : 127 - 133
  • [2] Efficacy of Defending Deep Neural Networks against Adversarial Attacks with Randomization
    Zhou, Yan
    Kantarcioglu, Murat
    Xi, Bowei
    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS II, 2020, 11413
  • [3] HRAE: Hardware-assisted Randomization against Adversarial Example Attacks
    Zhang, Jiliang
    Peng, Shuang
    Hu, Yupeng
    Peng, Fei
    Hu, Wei
    Lai, Jinmei
    Ye, Jing
    Wang, Xiangqi
    2020 IEEE 29TH ASIAN TEST SYMPOSIUM (ATS), 2020, : 36 - 41
  • [4] Practical Adversarial Attacks Against Speaker Recognition Systems
    Li, Zhuohang
    Shi, Cong
    Xie, Yi
    Liu, Jian
    Yuan, Bo
    Chen, Yingying
    PROCEEDINGS OF THE 21ST INTERNATIONAL WORKSHOP ON MOBILE COMPUTING SYSTEMS AND APPLICATIONS (HOTMOBILE'20), 2020, : 9 - 14
  • [5] Adversarial Attacks Against Face Recognition: A Comprehensive Study
    Vakhshiteh, Fatemeh
    Nickabadi, Ahmad
    Ramachandra, Raghavendra
    IEEE ACCESS, 2021, 9 : 92735 - 92756
  • [6] Imperceptible adversarial attacks against traffic scene recognition
    Zhu, Yinghui
    Jiang, Yuzhen
    SOFT COMPUTING, 2021, 25 (20) : 13069 - 13077
  • [7] Universal Adversarial Spoofing Attacks against Face Recognition
    Amada, Takuma
    Liew, Seng Pei
    Kakizaki, Kazuya
    Araki, Toshinori
    2021 INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB 2021), 2021,
  • [8] Resilient Distributed Optimization Algorithm Against Adversarial Attacks
    Zhao, Chengcheng
    He, Jianping
    Wang, Qing-Guo
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 65 (10) : 4308 - 4315
  • [9] GAN-based classifier protection against adversarial attacks
    Liu, Shuqi
    Shao, Mingwen
    Liu, Xinping
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (05) : 7085 - 7095
  • [10] A quantum active learning algorithm for sampling against adversarial attacks
    Casares, P. A. M.
    Martin-Delgado, M. A.
    NEW JOURNAL OF PHYSICS, 2020, 22 (07)