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Detecting AutoAttack Perturbations in the Frequency Domain
v1v2v3 (latest)

Detecting AutoAttack Perturbations in the Frequency Domain

16 November 2021
P. Lorenz
P. Harder
Dominik Strassel
Margret Keuper
J. Keuper
    AAML
ArXiv (abs)PDFHTML

Papers citing "Detecting AutoAttack Perturbations in the Frequency Domain"

12 / 12 papers shown
Title
Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks
Het Patel
Muzammil Allie
Qian Zhang
Jia Chen
Evangelos E. Papalexakis
AAMLVLM
52
0
0
19 Sep 2025
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing
  Diagnostic Accuracy in Alzheimer's Case Studies
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies
Mohammad Hossein Najafi
Mohammad Morsali
Mohammadmahdi Vahediahmar
Saeed Bagheri Shouraki
AAMLMedIm
245
0
0
16 Aug 2024
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable
  Adversarial Attacks
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks
Hunmin Yang
Jongoh Jeong
Kuk-Jin Yoon
AAML
302
9
0
30 Jul 2024
Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks
Prompt-Driven Contrastive Learning for Transferable Adversarial AttacksEuropean Conference on Computer Vision (ECCV), 2024
Hunmin Yang
Jongoh Jeong
Kuk-Jin Yoon
AAMLVLM
379
9
0
30 Jul 2024
Towards a Novel Perspective on Adversarial Examples Driven by Frequency
Towards a Novel Perspective on Adversarial Examples Driven by Frequency
Zhun Zhang
Yi Zeng
Qihe Liu
Shijie Zhou
AAML
168
3
0
16 Apr 2024
Adversarial Examples are Misaligned in Diffusion Model Manifolds
Adversarial Examples are Misaligned in Diffusion Model ManifoldsIEEE International Joint Conference on Neural Network (IJCNN), 2024
P. Lorenz
Ricard Durall
Jansi Keuper
DiffM
345
1
0
12 Jan 2024
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial
  Detection
Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial DetectionVISIGRAPP (VISIGRAPP), 2022
P. Lorenz
Margret Keuper
J. Keuper
AAML
332
7
0
13 Dec 2022
Robust Models are less Over-Confident
Robust Models are less Over-ConfidentNeural Information Processing Systems (NeurIPS), 2022
Julia Grabinski
Paul Gavrikov
J. Keuper
Margret Keuper
AAML
200
28
0
12 Oct 2022
DAD: Data-free Adversarial Defense at Test Time
DAD: Data-free Adversarial Defense at Test Time
Gaurav Kumar Nayak
Ruchit Rawal
Anirban Chakraborty
AAML
154
12
0
04 Apr 2022
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial
  Robustness?
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?
P. Lorenz
Dominik Strassel
Margret Keuper
J. Keuper
AAML
252
11
0
02 Dec 2021
A Frequency Perspective of Adversarial Robustness
A Frequency Perspective of Adversarial Robustness
Shishira R. Maiya
Max Ehrlich
Vatsal Agarwal
Ser-Nam Lim
Tom Goldstein
Abhinav Shrivastava
AAML
152
44
0
26 Oct 2021
Frequency Centric Defense Mechanisms against Adversarial Examples
Frequency Centric Defense Mechanisms against Adversarial Examples
Sanket B. Shah
Param Raval
Harin Khakhi
M. Raval
AAML
190
7
0
26 Oct 2021
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