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Adversarial Examples Are Not Bugs, They Are Features
v1v2v3v4 (latest)

Adversarial Examples Are Not Bugs, They Are Features

Neural Information Processing Systems (NeurIPS), 2019
6 May 2019
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Logan Engstrom
Brandon Tran
Aleksander Madry
    SILM
ArXiv (abs)PDFHTML

Papers citing "Adversarial Examples Are Not Bugs, They Are Features"

50 / 1,093 papers shown
Erasing, Transforming, and Noising Defense Network for Occluded Person
  Re-Identification
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Neng Dong
Liyan Zhang
Shuanglin Yan
Hao Tang
Jinhui Tang
AAML
432
60
0
14 Jul 2023
Vulnerability-Aware Instance Reweighting For Adversarial Training
Vulnerability-Aware Instance Reweighting For Adversarial Training
Olukorede Fakorede
Ashutosh Nirala
Modeste Atsague
Jin Tian
AAML
171
2
0
14 Jul 2023
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for
  Test-Time Policy Adaptation
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy AdaptationInternational Conference on Machine Learning (ICML), 2023
Andi Peng
Aviv Netanyahu
Mark K. Ho
Tianmin Shu
Andreea Bobu
J. Shah
Pulkit Agrawal
333
17
0
12 Jul 2023
A Theoretical Perspective on Subnetwork Contributions to Adversarial
  Robustness
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness
Jovon Craig
Joshua Andle
Theodore S. Nowak
Salimeh Yasaei Sekeh
AAML
153
0
0
07 Jul 2023
Kernels, Data & Physics
Kernels, Data & PhysicsJournal of Statistical Mechanics: Theory and Experiment (J. Stat. Mech.), 2023
Francesco Cagnetta
Deborah Oliveira
Mahalakshmi Sabanayagam
Nikolaos Tsilivis
Julia Kempe
240
0
0
05 Jul 2023
Transgressing the boundaries: towards a rigorous understanding of deep
  learning and its (non-)robustness
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness
C. Hartmann
Lorenz Richter
AAML
206
2
0
05 Jul 2023
Adversarial Learning in Real-World Fraud Detection: Challenges and
  Perspectives
Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
Daniele Lunghi
A. Simitsis
O. Caelen
Gianluca Bontempi
AAMLFaML
206
14
0
03 Jul 2023
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
Adnan Qayyum
Hassan Ali
Massimo Caputo
H. Vohra
Taofeek Akinosho
Sofiat Abioye
Ilhem Berrou
Paweł Capik
Junaid Qadir
Muhammad Bilal
225
0
0
03 Jul 2023
The Importance of Robust Features in Mitigating Catastrophic Forgetting
The Importance of Robust Features in Mitigating Catastrophic ForgettingInternational Symposium on Computers and Communications (ISCC), 2023
Hikmat Khan
N. Bouaynaya
Ghulam Rasool
203
9
0
29 Jun 2023
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning
Robust Proxy: Improving Adversarial Robustness by Robust Proxy LearningIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2023
Hong Joo Lee
Yonghyun Ro
AAML
177
4
0
27 Jun 2023
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
A Survey on Out-of-Distribution Evaluation of Neural NLP ModelsInternational Joint Conference on Artificial Intelligence (IJCAI), 2023
Xinzhe Li
Ming Liu
Shang Gao
Wray Buntine
225
24
0
27 Jun 2023
A Spectral Perspective towards Understanding and Improving Adversarial
  Robustness
A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Binxiao Huang
Rui Lin
Chaofan Tao
Ngai Wong
AAML
140
0
0
25 Jun 2023
On Evaluating the Adversarial Robustness of Semantic Segmentation Models
On Evaluating the Adversarial Robustness of Semantic Segmentation Models
L. Halmosi
Márk Jelasity
AAMLVLM
282
2
0
25 Jun 2023
Targeted Background Removal Creates Interpretable Feature Visualizations
Targeted Background Removal Creates Interpretable Feature VisualizationsMidwest Symposium on Circuits and Systems (MWSCAS), 2023
Ian E. Nielsen
Erik Grundeland
J. Snedeker
Ghulam Rasool
Ravichandran Ramachandran
FAttAAML
128
2
0
22 Jun 2023
Anticipatory Thinking Challenges in Open Worlds: Risk Management
Anticipatory Thinking Challenges in Open Worlds: Risk Management
Adam Amos-Binks
Dustin Dannenhauer
Leilani H. Gilpin
141
1
0
22 Jun 2023
Towards quantum enhanced adversarial robustness in machine learning
Towards quantum enhanced adversarial robustness in machine learningNature Machine Intelligence (Nat. Mach. Intell.), 2023
Maxwell T. West
S. Tsang
J. S. Low
C. Hill
C. Leckie
Lloyd C. L. Hollenberg
S. Erfani
Muhammad Usman
AAMLOOD
211
72
0
22 Jun 2023
A Comprehensive Study on the Robustness of Image Classification and
  Object Detection in Remote Sensing: Surveying and Benchmarking
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and BenchmarkingJournal of remote sensing (JRS), 2023
Shaohui Mei
Jiawei Lian
Xiaofei Wang
Yuru Su
Mingyang Ma
Lap-Pui Chau
AAML
375
14
0
21 Jun 2023
You Don't Need Robust Machine Learning to Manage Adversarial Attack
  Risks
You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks
Edward Raff
M. Benaroch
Andrew L. Farris
AAML
203
6
0
16 Jun 2023
Area is all you need: repeatable elements make stronger adversarial
  attacks
Area is all you need: repeatable elements make stronger adversarial attacks
D. Niederhut
AAML
196
0
0
13 Jun 2023
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis,
  and LLMs Evaluations
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs EvaluationsNeural Information Processing Systems (NeurIPS), 2023
Lifan Yuan
Yangyi Chen
Ganqu Cui
Hongcheng Gao
Fangyuan Zou
Xingyi Cheng
Heng Ji
Zhiyuan Liu
Maosong Sun
602
134
0
07 Jun 2023
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in
  Current XAI Research
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
Timo Freiesleben
Gunnar Konig
157
28
0
07 Jun 2023
Deep Classifier Mimicry without Data Access
Deep Classifier Mimicry without Data AccessInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Steven Braun
Martin Mundt
Kristian Kersting
DiffM
475
6
0
03 Jun 2023
What Can We Learn from Unlearnable Datasets?
What Can We Learn from Unlearnable Datasets?Neural Information Processing Systems (NeurIPS), 2023
Pedro Sandoval-Segura
Vasu Singla
Jonas Geiping
Micah Goldblum
Tom Goldstein
279
21
0
30 May 2023
From Adversarial Arms Race to Model-centric Evaluation: Motivating a
  Unified Automatic Robustness Evaluation Framework
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation FrameworkAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Yangyi Chen
Hongcheng Gao
Ganqu Cui
Lifan Yuan
Dehan Kong
...
Longtao Huang
H. Xue
Zhiyuan Liu
Maosong Sun
Heng Ji
AAMLELM
227
6
0
29 May 2023
Large Language Models Can be Lazy Learners: Analyze Shortcuts in
  In-Context Learning
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context LearningAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Ruixiang Tang
Dehan Kong
Lo-li Huang
Hui Xue
319
80
0
26 May 2023
DistriBlock: Identifying adversarial audio samples by leveraging
  characteristics of the output distribution
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distributionConference on Uncertainty in Artificial Intelligence (UAI), 2023
Matías P. Pizarro
D. Kolossa
Asja Fischer
AAML
520
2
0
26 May 2023
A Tale of Two Approximations: Tightening Over-Approximation for DNN
  Robustness Verification via Under-Approximation
A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-ApproximationInternational Symposium on Software Testing and Analysis (ISSTA), 2023
Zhiyi Xue
Si Liu
Zhaodi Zhang
Yiting Wu
Hao Fei
AAML
181
3
0
26 May 2023
On Evaluating Adversarial Robustness of Large Vision-Language Models
On Evaluating Adversarial Robustness of Large Vision-Language ModelsNeural Information Processing Systems (NeurIPS), 2023
Yunqing Zhao
Tianyu Pang
Chao Du
Xiao Yang
Chongxuan Li
Ngai-Man Cheung
Min Lin
VLMAAMLMLLM
485
266
0
26 May 2023
Enhancing Accuracy and Robustness through Adversarial Training in Class
  Incremental Continual Learning
Enhancing Accuracy and Robustness through Adversarial Training in Class Incremental Continual Learning
Minchan Kwon
Kangil Kim
AAML
104
1
0
23 May 2023
Towards Benchmarking and Assessing Visual Naturalness of Physical World
  Adversarial Attacks
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial AttacksComputer Vision and Pattern Recognition (CVPR), 2023
Simin Li
Shuing Zhang
Gujun Chen
Dong Wang
Pu Feng
Jinyang Guo
Aishan Liu
Xin Yi
Xianglong Liu
AAML
192
25
0
22 May 2023
A Survey of Safety and Trustworthiness of Large Language Models through
  the Lens of Verification and Validation
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and ValidationArtificial Intelligence Review (AIR), 2023
Xiaowei Huang
Wenjie Ruan
Wei Huang
Gao Jin
Yizhen Dong
...
Sihao Wu
Peipei Xu
Dengyu Wu
André Freitas
Mustafa A. Mustafa
ALM
355
149
0
19 May 2023
Towards Generalizable Data Protection With Transferable Unlearnable
  Examples
Towards Generalizable Data Protection With Transferable Unlearnable Examples
Bin Fang
Yue Liu
Shuang Wu
Tianyi Zheng
Shouhong Ding
Ran Yi
Lizhuang Ma
185
6
0
18 May 2023
Re-thinking Data Availablity Attacks Against Deep Neural Networks
Re-thinking Data Availablity Attacks Against Deep Neural Networks
Bin Fang
Yue Liu
Shuang Wu
Ran Yi
Shouhong Ding
Lizhuang Ma
AAML
187
0
0
18 May 2023
On the ISS Property of the Gradient Flow for Single Hidden-Layer Neural
  Networks with Linear Activations
On the ISS Property of the Gradient Flow for Single Hidden-Layer Neural Networks with Linear Activations
A. C. B. D. Oliveira
Milad Siami
Eduardo Sontag
243
2
0
17 May 2023
Exploiting Frequency Spectrum of Adversarial Images for General
  Robustness
Exploiting Frequency Spectrum of Adversarial Images for General Robustness
Chun Yang Tan
K. Kawamoto
Hiroshi Kera
AAMLOOD
157
1
0
15 May 2023
Convolutional Neural Networks Rarely Learn Shape for Semantic
  Segmentation
Convolutional Neural Networks Rarely Learn Shape for Semantic SegmentationPattern Recognition (Pattern Recogn.), 2023
Yixin Zhang
Maciej A. Mazurowski
3DV3DPC
361
18
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11 May 2023
Randomized Smoothing with Masked Inference for Adversarially Robust Text
  Classifications
Randomized Smoothing with Masked Inference for Adversarially Robust Text ClassificationsAnnual Meeting of the Association for Computational Linguistics (ACL), 2023
Han Cheol Moon
Shafiq Joty
Ruochen Zhao
Megh Thakkar
Xu Chi
AAML
236
18
0
11 May 2023
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues
Even Small Correlation and Diversity Shifts Pose Dataset-Bias IssuesPattern Recognition Letters (PR), 2023
Alceu Bissoto
Catarina Barata
Eduardo Valle
Sandra Avila
OOD
223
9
0
09 May 2023
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
Zeming Wei
Jingyu Zhu
Yihao Zhang
AAML
216
17
0
09 May 2023
Physical Adversarial Attacks for Surveillance: A Survey
Physical Adversarial Attacks for Surveillance: A SurveyIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Kien Nguyen Thanh
Tharindu Fernando
Clinton Fookes
Sridha Sridharan
AAML
400
26
0
01 May 2023
Lyapunov-Stable Deep Equilibrium Models
Lyapunov-Stable Deep Equilibrium ModelsAAAI Conference on Artificial Intelligence (AAAI), 2023
Haoyu Chu
Shikui Wei
Ting Liu
Yao-Min Zhao
Yuto Miyatake
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186
8
0
25 Apr 2023
StyLess: Boosting the Transferability of Adversarial Examples
StyLess: Boosting the Transferability of Adversarial ExamplesComputer Vision and Pattern Recognition (CVPR), 2023
Kaisheng Liang
Bin Xiao
AAML
211
25
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Towards the Universal Defense for Query-Based Audio Adversarial Attacks
Towards the Universal Defense for Query-Based Audio Adversarial Attacks
Feng Guo
Zhengyi Sun
Yuxuan Chen
Lei Ju
AAML
175
4
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20 Apr 2023
Wavelets Beat Monkeys at Adversarial Robustness
Wavelets Beat Monkeys at Adversarial Robustness
Jingtong Su
Julia Kempe
AAMLOOD
123
2
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19 Apr 2023
Adversarial Examples from Dimensional Invariance
Adversarial Examples from Dimensional Invariance
Benjamin L. Badger
140
0
0
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Going Further: Flatness at the Rescue of Early Stopping for Adversarial
  Example Transferability
Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability
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Maxime Cordy
Yves Le Traon
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244
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Beyond Empirical Risk Minimization: Local Structure Preserving
  Regularization for Improving Adversarial Robustness
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness
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Jiahuan Zhou
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139
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29 Mar 2023
Visual Content Privacy Protection: A Survey
Visual Content Privacy Protection: A SurveyACM Computing Surveys (ACM Comput. Surv.), 2023
Ruoyu Zhao
Yushu Zhang
Tao Wang
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175
36
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29 Mar 2023
Personalized Federated Learning on Long-Tailed Data via Adversarial
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Pinxin Qian
Gang Huang
Hanzi Wang
184
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SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution
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SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Jingyang Zhang
Nathan Inkawhich
Randolph Linderman
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199
1
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