ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1908.07116
  4. Cited By
Protecting Neural Networks with Hierarchical Random Switching: Towards
  Better Robustness-Accuracy Trade-off for Stochastic Defenses

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

20 August 2019
Tianlin Li
Siyue Wang
Pin-Yu Chen
Yanzhi Wang
Brian Kulis
Xue Lin
S. Chin
    AAML
ArXivPDFHTML

Papers citing "Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses"

4 / 4 papers shown
Title
Boundary Defense Against Black-box Adversarial Attacks
Boundary Defense Against Black-box Adversarial Attacks
Manjushree B. Aithal
Xiaohua Li
AAML
21
6
0
31 Jan 2022
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation
Manjushree B. Aithal
Xiaohua Li
AAML
60
6
0
30 Sep 2021
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
Siyue Wang
Xiao Wang
Pin-Yu Chen
Pu Zhao
Xue Lin
AAML
38
2
0
14 May 2021
On the Design of Black-box Adversarial Examples by Leveraging
  Gradient-free Optimization and Operator Splitting Method
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Pu Zhao
Sijia Liu
Pin-Yu Chen
Nghia Hoang
Kaidi Xu
B. Kailkhura
Xue Lin
AAML
27
54
0
26 Jul 2019
1