Why is Your Trojan NOT Responding? A Quantitative Analysis of Failures in Backdoor Attacks of Neural Networks

被引:0
|
作者
Hu, Xingbo [1 ,2 ]
Lan, Yibing [1 ,2 ]
Gao, Ruimin [3 ]
Meng, Guozhu [1 ,2 ]
Chen, Kai [1 ,2 ]
机构
[1] SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
[2] School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
[3] Mathematics and Statistics, University of Victoria, Victoria, Canada
关键词
Compilation and indexing terms; Copyright 2024 Elsevier Inc;
D O I
暂无
中图分类号
学科分类号
摘要
Statistical tests - Learning systems - Failure (mechanical) - Malware
引用
收藏
页码:754 / 771
相关论文
共 50 条
  • [31] More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
    Xu, Jing
    Wang, Rui
    Koffas, Stefanos
    Liang, Kaitai
    Picek, Stjepan
    PROCEEDINGS OF THE 38TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, ACSAC 2022, 2022, : 684 - 698
  • [32] Diffense: Defense Against Backdoor Attacks on Deep Neural Networks With Latent Diffusion
    Hu, Bowen
    Chang, Chip-Hong
    IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2024, 14 (04) : 729 - 742
  • [33] Latent Space-Based Backdoor Attacks Against Deep Neural Networks
    Kristanto, Adrian
    Wang, Shuo
    Rudolph, Carsten
    2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022,
  • [34] Kaleidoscope: Physical Backdoor Attacks Against Deep Neural Networks With RGB Filters
    Gong, Xueluan
    Wang, Ziyao
    Chen, Yanjiao
    Xue, Meng
    Wang, Qian
    Shen, Chao
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2023, 20 (06) : 4993 - 5004
  • [35] Robust Backdoor Attacks against Deep Neural Networks in Real Physical World
    Xue, Mingfu
    He, Can
    Sun, Shichang
    Wang, Jian
    Liu, Weiqiang
    2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021), 2021, : 620 - 626
  • [36] Interpretability-Guided Defense Against Backdoor Attacks to Deep Neural Networks
    Jiang, Wei
    Wen, Xiangyu
    Zhan, Jinyu
    Wang, Xupeng
    Song, Ziwei
    IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 41 (08) : 2611 - 2624
  • [37] Backdoor Attacks in Neural Networks - A Systematic Evaluation on Multiple Traffic Sign Datasets
    Rehman, Huma
    Ekelhart, Andreas
    Mayer, Rudolf
    MACHINE LEARNING AND KNOWLEDGE EXTRACTION, CD-MAKE 2019, 2019, 11713 : 285 - 300
  • [38] Detection of backdoor attacks using targeted universal adversarial perturbations for deep neural networks
    Qu, Yubin
    Huang, Song
    Chen, Xiang
    Wang, Xingya
    Yao, Yongming
    JOURNAL OF SYSTEMS AND SOFTWARE, 2024, 207
  • [39] Unveiling the Threat: Investigating Distributed and Centralized Backdoor Attacks in Federated Graph Neural Networks
    Xu, Jing
    Koffas, Stefanos
    Picek, Stjepan
    DIGITAL THREATS: RESEARCH AND PRACTICE, 2024, 5 (02):
  • [40] Combining Defences Against Data-Poisoning Based Backdoor Attacks on Neural Networks
    Milakovic, Andrea
    Mayer, Rudolf
    DATA AND APPLICATIONS SECURITY AND PRIVACY XXXVI, DBSEC 2022, 2022, 13383 : 28 - 47