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. 2104.15064
  4. Cited By
Black-box adversarial attacks using Evolution Strategies

Black-box adversarial attacks using Evolution Strategies

30 April 2021
Hao Qiu
Leonardo Lucio Custode
Giovanni Iacca
    AAML
ArXivPDFHTML

Papers citing "Black-box adversarial attacks using Evolution Strategies"

12 / 12 papers shown
Title
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
Yongyi Su
Yushu Li
Nanqing Liu
Kui Jia
Xulei Yang
Chuan-Sheng Foo
Xun Xu
TTA
AAML
56
0
0
07 Oct 2024
Sparse vs Contiguous Adversarial Pixel Perturbations in Multimodal
  Models: An Empirical Analysis
Sparse vs Contiguous Adversarial Pixel Perturbations in Multimodal Models: An Empirical Analysis
Cristian-Alexandru Botocan
Raphael Meier
Ljiljana Dolamic
AAML
19
0
0
25 Jul 2024
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by
  Evolving Adversarial Instances
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
Emma Hart
Quentin Renau
Kevin Sim
M. Alissa
AAML
22
0
0
24 Jun 2024
Evolutionary Computation and Explainable AI: A Roadmap to Transparent
  Intelligent Systems
Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems
Ryan Zhou
Jaume Bacardit
Alexander Brownlee
Stefano Cagnoni
Martin Fyvie
Giovanni Iacca
John Mccall
N. V. Stein
David Walker
Ting-Kuei Hu
37
0
0
12 Jun 2024
EGAN: Evolutional GAN for Ransomware Evasion
EGAN: Evolutional GAN for Ransomware Evasion
Daniel Commey
Benjamin Appiah
B. K. Frimpong
Isaac Osei
Ebenezer N. A. Hammond
Garth V. Crosby
AAML
GAN
24
0
0
20 May 2024
Machine Learning Robustness: A Primer
Machine Learning Robustness: A Primer
Houssem Ben Braiek
Foutse Khomh
AAML
OOD
34
5
0
01 Apr 2024
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
Ben Pinhasov
Raz Lapid
Rony Ohayon
Moshe Sipper
Y. Aperstein
AAML
27
7
0
05 Mar 2024
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep
  Learning Models
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models
Viet Vo
Ehsan Abbasnejad
D. Ranasinghe
AAML
22
14
0
31 Jan 2022
When and How to Fool Explainable Models (and Humans) with Adversarial
  Examples
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Jon Vadillo
Roberto Santana
Jose A. Lozano
SILM
AAML
33
11
0
05 Jul 2021
GreedyFool: Multi-Factor Imperceptibility and Its Application to
  Designing a Black-box Adversarial Attack
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack
Hui Liu
Bo Zhao
Minzhi Ji
Peng Liu
AAML
24
6
0
14 Oct 2020
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
Minhao Cheng
Simranjit Singh
Patrick H. Chen
Pin-Yu Chen
Sijia Liu
Cho-Jui Hsieh
AAML
122
219
0
24 Sep 2019
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
278
5,833
0
08 Jul 2016
1