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Understanding and Improving Fast Adversarial Training
v1v2 (latest)

Understanding and Improving Fast Adversarial Training

6 July 2020
Maksym Andriushchenko
Nicolas Flammarion
    AAML
ArXiv (abs)PDFHTMLGithub (95★)

Papers citing "Understanding and Improving Fast Adversarial Training"

50 / 200 papers shown
DISCO: Adversarial Defense with Local Implicit Functions
DISCO: Adversarial Defense with Local Implicit FunctionsNeural Information Processing Systems (NeurIPS), 2022
Chih-Hui Ho
Nuno Vasconcelos
AAML
410
53
0
11 Dec 2022
Understanding and Combating Robust Overfitting via Input Loss Landscape
  Analysis and Regularization
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and RegularizationPattern Recognition (Pattern Recogn.), 2022
Lin Li
Michael W. Spratling
AAML
224
44
0
09 Dec 2022
Advancing Deep Metric Learning Through Multiple Batch Norms And
  Multi-Targeted Adversarial Examples
Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples
Inderjeet Singh
Kazuya Kakizaki
Toshinori Araki
AAMLOOD
201
0
0
29 Nov 2022
Towards More Robust Interpretation via Local Gradient Alignment
Towards More Robust Interpretation via Local Gradient AlignmentAAAI Conference on Artificial Intelligence (AAAI), 2022
Sunghwan Joo
Seokhyeon Jeong
Juyeon Heo
Adrian Weller
Taesup Moon
FAtt
247
7
0
29 Nov 2022
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify
  Proprietary Dataset Use in Deep Neural Networks
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural NetworksAsia-Pacific Computer Systems Architecture Conference (ACSA), 2022
Seonhye Park
A. Abuadbba
Shuo Wang
Kristen Moore
Yansong Gao
Hyoungshick Kim
Surya Nepal
AAML
111
3
0
24 Nov 2022
Efficient Adversarial Training with Robust Early-Bird Tickets
Efficient Adversarial Training with Robust Early-Bird TicketsConference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Zhiheng Xi
Rui Zheng
Tao Gui
Tao Gui
Xuanjing Huang
AAML
241
9
0
14 Nov 2022
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for
  Improving Adversarial Training
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial TrainingComputer Vision and Pattern Recognition (CVPR), 2022
Junhao Dong
Seyed-Mohsen Moosavi-Dezfooli
Jianhuang Lai
Xiaohua Xie
AAML
281
42
0
01 Nov 2022
Adversarial Training with Complementary Labels: On the Benefit of
  Gradually Informative Attacks
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative AttacksNeural Information Processing Systems (NeurIPS), 2022
Jianan Zhou
Jianing Zhu
Jingfeng Zhang
Tongliang Liu
Gang Niu
Bo Han
Masashi Sugiyama
AAML
140
11
0
01 Nov 2022
AccelAT: A Framework for Accelerating the Adversarial Training of Deep
  Neural Networks through Accuracy Gradient
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy GradientIEEE Access (IEEE Access), 2022
F. Nikfam
Alberto Marchisio
Maurizio Martina
Mohamed Bennai
AAML
184
1
0
13 Oct 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
259
31
0
12 Oct 2022
Stable and Efficient Adversarial Training through Local Linearization
Stable and Efficient Adversarial Training through Local Linearization
Zhuorong Li
Daiwei Yu
AAML
109
0
0
11 Oct 2022
Adversarial Coreset Selection for Efficient Robust Training
Adversarial Coreset Selection for Efficient Robust TrainingInternational Journal of Computer Vision (IJCV), 2022
H. M. Dolatabadi
S. Erfani
C. Leckie
AAML
203
11
0
13 Sep 2022
FADE: Enabling Federated Adversarial Training on Heterogeneous
  Resource-Constrained Edge Devices
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
Minxue Tang
Jianyi Zhang
Mingyuan Ma
Louis DiValentin
Aolin Ding
Amin Hassanzadeh
Xue Yang
Yiran Chen
FedML
147
0
0
08 Sep 2022
Bag of Tricks for FGSM Adversarial Training
Bag of Tricks for FGSM Adversarial Training
Zichao Li
Li Liu
Zeyu Wang
Yuyin Zhou
Cihang Xie
AAML
153
6
0
06 Sep 2022
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective
  for Adversarial Training
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training
Zihui Wu
Haichang Gao
Bingqian Zhou
Xiaoyan Guo
Shudong Zhang
AAML
191
0
0
26 Aug 2022
Adversarial Vulnerability of Temporal Feature Networks for Object
  Detection
Adversarial Vulnerability of Temporal Feature Networks for Object Detection
Svetlana Pavlitskaya
Nikolai Polley
Michael Weber
J. Marius Zöllner
AAML
176
7
0
23 Aug 2022
Enhancing Diffusion-Based Image Synthesis with Robust Classifier
  Guidance
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
Bahjat Kawar
Roy Ganz
Michael Elad
DiffM
186
46
0
18 Aug 2022
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and
  Boosting Segmentation Robustness
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation RobustnessEuropean Conference on Computer Vision (ECCV), 2022
Jindong Gu
Hengshuang Zhao
Volker Tresp
Juil Sock
AAML
287
91
0
25 Jul 2022
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
Do Perceptually Aligned Gradients Imply Adversarial Robustness?International Conference on Machine Learning (ICML), 2022
Roy Ganz
Bahjat Kawar
Michael Elad
AAML
307
15
0
22 Jul 2022
Towards Efficient Adversarial Training on Vision Transformers
Towards Efficient Adversarial Training on Vision TransformersEuropean Conference on Computer Vision (ECCV), 2022
Boxi Wu
Jindong Gu
Zhifeng Li
Deng Cai
Xiaofei He
Wei Liu
ViTAAML
253
45
0
21 Jul 2022
Prior-Guided Adversarial Initialization for Fast Adversarial Training
Prior-Guided Adversarial Initialization for Fast Adversarial TrainingEuropean Conference on Computer Vision (ECCV), 2022
Yang Liu
Yong Zhang
Xingxing Wei
Baoyuan Wu
Ke Ma
Jue Wang
Xiaochun Cao
AAML
192
50
0
18 Jul 2022
Understanding Robust Learning through the Lens of Representation
  Similarities
Understanding Robust Learning through the Lens of Representation SimilaritiesNeural Information Processing Systems (NeurIPS), 2022
Christian Cianfarani
A. Bhagoji
Vikash Sehwag
Ben Y. Zhao
Prateek Mittal
Haitao Zheng
OOD
324
18
0
20 Jun 2022
Catastrophic overfitting can be induced with discriminative non-robust
  features
Catastrophic overfitting can be induced with discriminative non-robust features
Guillermo Ortiz-Jiménez
Pau de Jorge
Amartya Sanyal
Adel Bibi
P. Dokania
P. Frossard
Grégory Rogez
Juil Sock
AAML
143
3
0
16 Jun 2022
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable RobustnessInternational Conference on Machine Learning (ICML), 2022
Tianlong Chen
Huan Zhang
Zhenyu Zhang
Shiyu Chang
Sijia Liu
Pin-Yu Chen
Zinan Lin
AAML
202
17
0
15 Jun 2022
Fast and Reliable Evaluation of Adversarial Robustness with
  Minimum-Margin Attack
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin AttackInternational Conference on Machine Learning (ICML), 2022
Ruize Gao
Jiongxiao Wang
Kaiwen Zhou
Yifan Zhang
Binghui Xie
Gang Niu
Bo Han
James Cheng
AAML
195
18
0
15 Jun 2022
Can pruning improve certified robustness of neural networks?
Can pruning improve certified robustness of neural networks?
Zhangheng Li
Tianlong Chen
Linyi Li
Yue Liu
Zinan Lin
AAML
233
17
0
15 Jun 2022
Distributed Adversarial Training to Robustify Deep Neural Networks at
  Scale
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleConference on Uncertainty in Artificial Intelligence (UAI), 2022
Gaoyuan Zhang
Songtao Lu
Yihua Zhang
Xiangyi Chen
Pin-Yu Chen
Quanfu Fan
Lee Martie
L. Horesh
Min-Fong Hong
Sijia Liu
OOD
273
14
0
13 Jun 2022
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Data-Efficient Double-Win Lottery Tickets from Robust Pre-trainingInternational Conference on Machine Learning (ICML), 2022
Tianlong Chen
Zhenyu Zhang
Sijia Liu
Yang Zhang
Shiyu Chang
Zinan Lin
AAML
142
8
0
09 Jun 2022
Fast Adversarial Training with Adaptive Step Size
Fast Adversarial Training with Adaptive Step SizeIEEE Transactions on Image Processing (IEEE TIP), 2022
Zhichao Huang
Yanbo Fan
Chen Liu
Weizhong Zhang
Yong Zhang
Mathieu Salzmann
Sabine Süsstrunk
Jue Wang
AAML
157
43
0
06 Jun 2022
On Trace of PGD-Like Adversarial Attacks
On Trace of PGD-Like Adversarial AttacksInternational Conference on Pattern Recognition (ICPR), 2022
Mo Zhou
Vishal M. Patel
AAML
279
4
0
19 May 2022
How Does Frequency Bias Affect the Robustness of Neural Image
  Classifiers against Common Corruption and Adversarial Perturbations?
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?International Joint Conference on Artificial Intelligence (IJCAI), 2022
Alvin Chan
Yew-Soon Ong
Clement Tan
AAML
174
15
0
09 May 2022
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms
  and Research Challenges
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
Zhenghua Chen
Ruibing Jin
Alvin Chan
Xiaoli Li
Yew-Soon Ong
175
10
0
08 May 2022
Rethinking Classifier and Adversarial Attack
Rethinking Classifier and Adversarial Attack
Youhuan Yang
Lei Sun
Leyu Dai
Song Guo
Xiuqing Mao
Xiaoqin Wang
Bayi Xu
AAML
132
0
0
04 May 2022
CE-based white-box adversarial attacks will not work using super-fitting
CE-based white-box adversarial attacks will not work using super-fitting
Youhuan Yang
Lei Sun
Leyu Dai
Song Guo
Xiuqing Mao
Xiaoqin Wang
Bayi Xu
AAML
279
0
0
04 May 2022
Fast AdvProp
Fast AdvPropInternational Conference on Learning Representations (ICLR), 2022
Jieru Mei
Yucheng Han
Yutong Bai
Yixiao Zhang
Yingwei Li
Xianhang Li
Alan Yuille
Cihang Xie
AAML
186
9
0
21 Apr 2022
Adversarial Robustness through the Lens of Convolutional Filters
Adversarial Robustness through the Lens of Convolutional Filters
Paul Gavrikov
J. Keuper
155
15
0
05 Apr 2022
FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting
FrequencyLowCut Pooling -- Plug & Play against Catastrophic OverfittingEuropean Conference on Computer Vision (ECCV), 2022
Julia Grabinski
Steffen Jung
J. Keuper
Margret Keuper
AAML
199
33
0
01 Apr 2022
CNN Filter DB: An Empirical Investigation of Trained Convolutional
  Filters
CNN Filter DB: An Empirical Investigation of Trained Convolutional FiltersComputer Vision and Pattern Recognition (CVPR), 2022
Paul Gavrikov
J. Keuper
AAML
229
36
0
29 Mar 2022
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization
  Perspective
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization PerspectiveInternational Conference on Learning Representations (ICLR), 2022
Yimeng Zhang
Yuguang Yao
Jinghan Jia
Jinfeng Yi
Min-Fong Hong
Shiyu Chang
Sijia Liu
AAML
316
39
0
27 Mar 2022
A Survey of Robust Adversarial Training in Pattern Recognition:
  Fundamental, Theory, and Methodologies
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and MethodologiesPattern Recognition (Pattern Recogn.), 2022
Zhuang Qian
Kaizhu Huang
Qiufeng Wang
Xu-Yao Zhang
OODAAMLObjD
246
94
0
26 Mar 2022
Task-Agnostic Robust Representation Learning
Task-Agnostic Robust Representation Learning
A. Nguyen
Ser Nam Lim
Juil Sock
SSLOOD
63
4
0
15 Mar 2022
On the benefits of knowledge distillation for adversarial robustness
On the benefits of knowledge distillation for adversarial robustness
Javier Maroto
Guillermo Ortiz-Jiménez
P. Frossard
AAMLFedML
192
27
0
14 Mar 2022
Adversarial amplitude swap towards robust image classifiers
Adversarial amplitude swap towards robust image classifiers
Tan Yang
K. Kawamoto
Hiroshi Kera
AAML
147
1
0
14 Mar 2022
Why adversarial training can hurt robust accuracy
Why adversarial training can hurt robust accuracyInternational Conference on Learning Representations (ICLR), 2022
Jacob Clarysse
Julia Hörrmann
Fanny Yang
AAML
230
22
0
03 Mar 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
Evaluating the Adversarial Robustness of Adaptive Test-time DefensesInternational Conference on Machine Learning (ICML), 2022
Francesco Croce
Sven Gowal
T. Brunner
Evan Shelhamer
Matthias Hein
A. Cemgil
TTAAAML
426
80
0
28 Feb 2022
ARIA: Adversarially Robust Image Attribution for Content Provenance
ARIA: Adversarially Robust Image Attribution for Content Provenance
Maksym Andriushchenko
Xiaochen Li
Geoffrey Oxholm
Thomas Gittings
Tu Bui
Nicolas Flammarion
John Collomosse
AAML
172
4
0
25 Feb 2022
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial
  Robustness
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial RobustnessInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Beomsu Kim
Junghoon Seo
AAML
200
0
0
21 Feb 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Robustness and Accuracy Could Be Reconcilable by (Proper) DefinitionInternational Conference on Machine Learning (ICML), 2022
Tianyu Pang
Min Lin
Xiao Yang
Junyi Zhu
Shuicheng Yan
416
150
0
21 Feb 2022
The Adversarial Security Mitigations of mmWave Beamforming Prediction
  Models using Defensive Distillation and Adversarial Retraining
The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
Murat Kuzlu
Ferhat Ozgur Catak
Umit Cali
Evren Çatak
Ozgur Guler
AAML
175
12
0
16 Feb 2022
Random Walks for Adversarial Meshes
Random Walks for Adversarial MeshesInternational Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), 2022
Amir Belder
Gal Yefet
Ran Ben Izhak
A. Tal
AAML
189
2
0
15 Feb 2022
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