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. 2303.01870
  4. Cited By
Revisiting Adversarial Training for ImageNet: Architectures, Training
  and Generalization across Threat Models

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models

3 March 2023
Naman D. Singh
Francesco Croce
Matthias Hein
    OOD
ArXivPDFHTML

Papers citing "Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models"

20 / 20 papers shown
Title
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
Hanxun Huang
Sarah Monazam Erfani
Yige Li
Xingjun Ma
James Bailey
AAML
44
0
0
08 May 2025
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain
Gaozheng Pei
Ke Ma
Yingfei Sun
Qianqian Xu
Q. Huang
DiffM
40
0
0
02 May 2025
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks
Jin Li
Ziqiang He
Anwei Luo
Jian-Fang Hu
Zhong Wang
Xiangui Kang
DiffM
66
0
0
12 Mar 2025
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
Mingkun Zhang
Keping Bi
Wei Chen
J. Guo
Xueqi Cheng
BDL
VLM
52
1
0
25 Feb 2025
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness
Emanuele Ballarin
A. Ansuini
Luca Bortolussi
AAML
62
0
0
20 Feb 2025
Trading Inference-Time Compute for Adversarial Robustness
Trading Inference-Time Compute for Adversarial Robustness
Wojciech Zaremba
Evgenia Nitishinskaya
Boaz Barak
Stephanie Lin
Sam Toyer
...
Rachel Dias
Eric Wallace
Kai Y. Xiao
Johannes Heidecke
Amelia Glaese
LRM
AAML
96
15
0
31 Jan 2025
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
Mohamed Djilani
Salah Ghamizi
Maxime Cordy
43
0
0
31 Dec 2024
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Yong Xie
Weijie Zheng
Hanxun Huang
Guangnan Ye
Xingjun Ma
AAML
72
1
0
20 Nov 2024
Guardians of Image Quality: Benchmarking Defenses Against Adversarial
  Attacks on Image Quality Metrics
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics
Alexander Gushchin
Khaled Abud
Georgii Bychkov
E. Shumitskaya
Anna Chistyakova
Sergey Lavrushkin
Bader Rasheed
Kirill Malyshev
D. Vatolin
Anastasia Antsiferova
AAML
46
2
0
02 Aug 2024
A Random Ensemble of Encrypted Vision Transformers for Adversarially
  Robust Defense
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense
Ryota Iijima
Sayaka Shiota
Hitoshi Kiya
36
6
0
11 Feb 2024
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
Xiaoyun Xu
Shujian Yu
Jingzheng Wu
S. Picek
AAML
35
0
0
08 Dec 2023
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial
  Defense
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense
Zunzhi You
Daochang Liu
Bohyung Han
Chang Xu
AAML
VLM
52
4
0
02 Feb 2023
Patches Are All You Need?
Patches Are All You Need?
Asher Trockman
J. Zico Kolter
ViT
225
402
0
24 Jan 2022
Are Transformers More Robust Than CNNs?
Are Transformers More Robust Than CNNs?
Yutong Bai
Jieru Mei
Alan Yuille
Cihang Xie
ViT
AAML
192
257
0
10 Nov 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural
  Networks
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Hanxun Huang
Yisen Wang
S. Erfani
Quanquan Gu
James Bailey
Xingjun Ma
AAML
TPM
46
100
0
07 Oct 2021
ResNet strikes back: An improved training procedure in timm
ResNet strikes back: An improved training procedure in timm
Ross Wightman
Hugo Touvron
Hervé Jégou
AI4TS
212
487
0
01 Oct 2021
MLP-Mixer: An all-MLP Architecture for Vision
MLP-Mixer: An all-MLP Architecture for Vision
Ilya O. Tolstikhin
N. Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
...
Andreas Steiner
Daniel Keysers
Jakob Uszkoreit
Mario Lucic
Alexey Dosovitskiy
271
2,603
0
04 May 2021
Emerging Properties in Self-Supervised Vision Transformers
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron
Hugo Touvron
Ishan Misra
Hervé Jégou
Julien Mairal
Piotr Bojanowski
Armand Joulin
320
5,785
0
29 Apr 2021
Mind the box: $l_1$-APGD for sparse adversarial attacks on image
  classifiers
Mind the box: l1l_1l1​-APGD for sparse adversarial attacks on image classifiers
Francesco Croce
Matthias Hein
AAML
47
54
0
01 Mar 2021
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
231
677
0
19 Oct 2020
1