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Adversarial Robustness Against the Union of Multiple Perturbation Models

Adversarial Robustness Against the Union of Multiple Perturbation Models

9 September 2019
Pratyush Maini
Eric Wong
J. Zico Kolter
    OOD
    AAML
ArXivPDFHTML

Papers citing "Adversarial Robustness Against the Union of Multiple Perturbation Models"

44 / 44 papers shown
Title
Towards a constructive framework for control theory
Towards a constructive framework for control theory
Pavel Osinenko
28
2
0
04 Jan 2025
Towards Universal Certified Robustness with Multi-Norm Training
Towards Universal Certified Robustness with Multi-Norm Training
Enyi Jiang
Gagandeep Singh
Gagandeep Singh
AAML
65
1
0
03 Oct 2024
RAMP: Boosting Adversarial Robustness Against Multiple $l_p$
  Perturbations
RAMP: Boosting Adversarial Robustness Against Multiple lpl_plp​ Perturbations
Enyi Jiang
Gagandeep Singh
AAML
35
1
0
09 Feb 2024
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive
  Attackers for Security Applications
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications
Hangsheng Zhang
Jiqiang Liu
Jinsong Dong
AAML
23
1
0
20 Jan 2024
SCAAT: Improving Neural Network Interpretability via Saliency
  Constrained Adaptive Adversarial Training
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training
Rui Xu
Wenkang Qin
Peixiang Huang
Hao Wang
Lin Luo
FAtt
AAML
43
2
0
09 Nov 2023
Optimization and Optimizers for Adversarial Robustness
Optimization and Optimizers for Adversarial Robustness
Hengyue Liang
Buyun Liang
Le Peng
Ying Cui
Tim Mitchell
Ju Sun
AAML
30
5
0
23 Mar 2023
Decentralized Adversarial Training over Graphs
Decentralized Adversarial Training over Graphs
Ying Cao
Elsa Rizk
Stefan Vlaski
Ali H. Sayed
AAML
48
1
0
23 Mar 2023
Multi-Agent Adversarial Training Using Diffusion Learning
Multi-Agent Adversarial Training Using Diffusion Learning
Ying Cao
Elsa Rizk
Stefan Vlaski
Ali H. Sayed
DiffM
40
4
0
03 Mar 2023
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks
Sihui Dai
Saeed Mahloujifar
Chong Xiang
Vikash Sehwag
Pin-Yu Chen
Prateek Mittal
AAML
OOD
39
7
0
21 Feb 2023
Multiple Perturbation Attack: Attack Pixelwise Under Different
  $\ell_p$-norms For Better Adversarial Performance
Multiple Perturbation Attack: Attack Pixelwise Under Different ℓp\ell_pℓp​-norms For Better Adversarial Performance
Ngoc N. Tran
Anh Tuan Bui
Dinh Q. Phung
Trung Le
AAML
36
1
0
05 Dec 2022
Game Theoretic Mixed Experts for Combinational Adversarial Machine
  Learning
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Ethan Rathbun
Kaleel Mahmood
Sohaib Ahmad
Caiwen Ding
Marten van Dijk
AAML
24
4
0
26 Nov 2022
Towards Generating Adversarial Examples on Mixed-type Data
Towards Generating Adversarial Examples on Mixed-type Data
Han Xu
Menghai Pan
Zhimeng Jiang
Huiyuan Chen
Xiaoting Li
Mahashweta Das
Hao Yang
AAML
SILM
23
0
0
17 Oct 2022
Towards Out-of-Distribution Adversarial Robustness
Towards Out-of-Distribution Adversarial Robustness
Adam Ibrahim
Charles Guille-Escuret
Ioannis Mitliagkas
Irina Rish
David M. Krueger
P. Bashivan
OOD
36
6
0
06 Oct 2022
A Closer Look at Robustness to L-infinity and Spatial Perturbations and
  their Composition
A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition
Luke Rowe
Benjamin Thérien
Krzysztof Czarnecki
Hongyang R. Zhang
OOD
30
0
0
05 Oct 2022
Adaptive Smoothness-weighted Adversarial Training for Multiple
  Perturbations with Its Stability Analysis
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis
Jiancong Xiao
Zeyu Qin
Yanbo Fan
Baoyuan Wu
Jue Wang
Zhimin Luo
AAML
42
7
0
02 Oct 2022
Adversarial Robustness for Tabular Data through Cost and Utility
  Awareness
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
Klim Kireev
B. Kulynych
Carmela Troncoso
AAML
26
16
0
27 Aug 2022
Semi-supervised Semantics-guided Adversarial Training for Trajectory
  Prediction
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
Ruochen Jiao
Xiangguo Liu
Takami Sato
Qi Alfred Chen
Qi Zhu
AAML
43
20
0
27 May 2022
Robustness through Cognitive Dissociation Mitigation in Contrastive
  Adversarial Training
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training
Adir Rahamim
I. Naeh
AAML
35
1
0
16 Mar 2022
On the Effectiveness of Adversarial Training against Backdoor Attacks
On the Effectiveness of Adversarial Training against Backdoor Attacks
Yinghua Gao
Dongxian Wu
Jingfeng Zhang
Guanhao Gan
Shutao Xia
Gang Niu
Masashi Sugiyama
AAML
32
22
0
22 Feb 2022
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding
  Style Transformation
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
Zhen Li
Guenevere Chen
Chen
Chen Chen
Yayi Zou
Shouhuai Xu
AAML
AI4TS
28
44
0
12 Feb 2022
Certifying Model Accuracy under Distribution Shifts
Certifying Model Accuracy under Distribution Shifts
Aounon Kumar
Alexander Levine
Tom Goldstein
S. Feizi
OOD
27
7
0
28 Jan 2022
Interpolated Joint Space Adversarial Training for Robust and
  Generalizable Defenses
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Chun Pong Lau
Jiang-Long Liu
Hossein Souri
Wei-An Lin
S. Feizi
Ramalingam Chellappa
AAML
34
12
0
12 Dec 2021
Mutual Adversarial Training: Learning together is better than going
  alone
Mutual Adversarial Training: Learning together is better than going alone
Jiang-Long Liu
Chun Pong Lau
Hossein Souri
S. Feizi
Ramalingam Chellappa
OOD
AAML
48
24
0
09 Dec 2021
Pareto Adversarial Robustness: Balancing Spatial Robustness and
  Sensitivity-based Robustness
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness
Ke Sun
Mingjie Li
Zhouchen Lin
AAML
27
2
0
03 Nov 2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust
  and Efficient Neural Networks
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Hassan Dbouk
Naresh R Shanbhag
AAML
21
7
0
28 Oct 2021
Trustworthy AI: From Principles to Practices
Trustworthy AI: From Principles to Practices
Bo Li
Peng Qi
Bo Liu
Shuai Di
Jingen Liu
Jiquan Pei
Jinfeng Yi
Bowen Zhou
121
357
0
04 Oct 2021
Local Intrinsic Dimensionality Signals Adversarial Perturbations
Local Intrinsic Dimensionality Signals Adversarial Perturbations
Sandamal Weerasinghe
T. Alpcan
S. Erfani
C. Leckie
Benjamin I. P. Rubinstein
AAML
23
0
0
24 Sep 2021
Advances in adversarial attacks and defenses in computer vision: A
  survey
Advances in adversarial attacks and defenses in computer vision: A survey
Naveed Akhtar
Ajmal Mian
Navid Kardan
M. Shah
AAML
41
236
0
01 Aug 2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure
  DNN Accelerators
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators
David Stutz
Nandhini Chandramoorthy
Matthias Hein
Bernt Schiele
AAML
MQ
26
18
0
16 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima
Relating Adversarially Robust Generalization to Flat Minima
David Stutz
Matthias Hein
Bernt Schiele
OOD
43
65
0
09 Apr 2021
Consistency Regularization for Adversarial Robustness
Consistency Regularization for Adversarial Robustness
Jihoon Tack
Sihyun Yu
Jongheon Jeong
Minseon Kim
Sung Ju Hwang
Jinwoo Shin
AAML
41
57
0
08 Mar 2021
Recent Advances in Adversarial Training for Adversarial Robustness
Recent Advances in Adversarial Training for Adversarial Robustness
Tao Bai
Jinqi Luo
Jun Zhao
Bihan Wen
Qian Wang
AAML
86
476
0
02 Feb 2021
A Comprehensive Evaluation Framework for Deep Model Robustness
A Comprehensive Evaluation Framework for Deep Model Robustness
Jun Guo
Wei Bao
Jiakai Wang
Yuqing Ma
Xing Gao
Gang Xiao
Aishan Liu
Zehao Zhao
Xianglong Liu
Wenjun Wu
AAML
ELM
38
55
0
24 Jan 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
234
681
0
19 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep
  Learning through Adversarial Robustness
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness
Guillermo Ortiz-Jiménez
Apostolos Modas
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
39
48
0
19 Oct 2020
Defending Against Multiple and Unforeseen Adversarial Videos
Defending Against Multiple and Unforeseen Adversarial Videos
Shao-Yuan Lo
Vishal M. Patel
AAML
31
23
0
11 Sep 2020
Measuring Robustness to Natural Distribution Shifts in Image
  Classification
Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori
Achal Dave
Vaishaal Shankar
Nicholas Carlini
Benjamin Recht
Ludwig Schmidt
OOD
50
537
0
01 Jul 2020
Adversarial Training against Location-Optimized Adversarial Patches
Adversarial Training against Location-Optimized Adversarial Patches
Sukrut Rao
David Stutz
Bernt Schiele
AAML
19
92
0
05 May 2020
Overfitting in adversarially robust deep learning
Overfitting in adversarially robust deep learning
Leslie Rice
Eric Wong
Zico Kolter
47
788
0
26 Feb 2020
Fast is better than free: Revisiting adversarial training
Fast is better than free: Revisiting adversarial training
Eric Wong
Leslie Rice
J. Zico Kolter
AAML
OOD
99
1,160
0
12 Jan 2020
Adversarial Attack Generation Empowered by Min-Max Optimization
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang
Tianyun Zhang
Sijia Liu
Pin-Yu Chen
Jiacen Xu
M. Fardad
Yangqiu Song
AAML
30
35
0
09 Jun 2019
A disciplined approach to neural network hyper-parameters: Part 1 --
  learning rate, batch size, momentum, and weight decay
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
L. Smith
208
1,020
0
26 Mar 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
251
1,842
0
03 Feb 2017
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
359
5,849
0
08 Jul 2016
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