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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
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off
  in Adversarial Training
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial TrainingInternational Conference on Learning Representations (ICLR), 2024
Shruthi Gowda
Bahram Zonooz
Elahe Arani
AAML
266
5
0
26 Jan 2024
Can overfitted deep neural networks in adversarial training generalize?
  -- An approximation viewpoint
Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
Zhongjie Shi
Fanghui Liu
Yuan Cao
Johan A. K. Suykens
236
0
0
24 Jan 2024
WPDA: Frequency-based Backdoor Attack with Wavelet Packet Decomposition
WPDA: Frequency-based Backdoor Attack with Wavelet Packet DecompositionNeural Networks (NN), 2024
Zhengyao Song
Yongqiang Li
Danni Yuan
Li Liu
Shaokui Wei
Baoyuan Wu
AAML
327
4
0
24 Jan 2024
Tight Verification of Probabilistic Robustness in Bayesian Neural
  Networks
Tight Verification of Probabilistic Robustness in Bayesian Neural NetworksInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Ben Batten
Mehran Hosseini
A. Lomuscio
AAML
288
9
0
21 Jan 2024
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
Siddharth Mansingh
Michal Kucer
Garrett Kenyon
Juston S. Moore
Michael Teti
AAML
269
2
0
21 Jan 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
259
1
0
20 Jan 2024
The Surprising Harmfulness of Benign Overfitting for Adversarial
  Robustness
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness
Yifan Hao
Tong Zhang
AAML
508
6
0
19 Jan 2024
Explainable and Transferable Adversarial Attack for ML-Based Network
  Intrusion Detectors
Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors
Hangsheng Zhang
Dongqi Han
Yinlong Liu
Zhiliang Wang
Jiyan Sun
Shangyuan Zhuang
Jiqiang Liu
Jinsong Dong
AAML
153
15
0
19 Jan 2024
Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to
  Identify Trajectory Prediction Vulnerabilities for Autonomous Driving
  Security
Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to Identify Trajectory Prediction Vulnerabilities for Autonomous Driving Security
Marsalis T. Gibson
David Babazadeh
Claire Tomlin
S. Shankar Sastry
AAML
290
1
0
18 Jan 2024
Mathematical Algorithm Design for Deep Learning under Societal and
  Judicial Constraints: The Algorithmic Transparency Requirement
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
Holger Boche
Adalbert Fono
Gitta Kutyniok
FaML
355
6
0
18 Jan 2024
Inductive Models for Artificial Intelligence Systems are Insufficient
  without Good Explanations
Inductive Models for Artificial Intelligence Systems are Insufficient without Good Explanations
Udesh Habaraduwa
102
0
0
17 Jan 2024
WAVES: Benchmarking the Robustness of Image Watermarks
WAVES: Benchmarking the Robustness of Image WatermarksInternational Conference on Machine Learning (ICML), 2024
Bang An
Mucong Ding
Tahseen Rabbani
Aakriti Agrawal
Yuancheng Xu
...
Sicheng Zhu
Abdirisak Mohamed
Yuxin Wen
Tom Goldstein
Furong Huang
442
71
0
16 Jan 2024
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy
  Compression on Audio and Image Models
Machine Perceptual Quality: Evaluating the Impact of Severe Lossy Compression on Audio and Image ModelsData Compression Conference (DCC), 2024
Dan G. Jacobellis
Daniel Cummings
N. Yadwadkar
184
2
0
15 Jan 2024
Structure-Preserving Physics-Informed Neural Networks With Energy or
  Lyapunov Structure
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov StructureInternational Joint Conference on Artificial Intelligence (IJCAI), 2024
Haoyu Chu
Yuto Miyatake
Wenjun Cui
Shikui Wei
Daisuke Furihata
PINN
206
4
0
10 Jan 2024
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual
  Concept Understanding
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding
Yatong Bai
Utsav Garg
Apaar Shanker
Haoming Zhang
Samyak Parajuli
...
Eugenia D Fomitcheva
E. Branson
Aerin Kim
Somayeh Sojoudi
Kyunghyun Cho
193
2
0
09 Jan 2024
Towards Explainable Artificial Intelligence (XAI): A Data Mining
  Perspective
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Haoyi Xiong
Xuhong Li
Xiaofei Zhang
Jiamin Chen
Xinhao Sun
Yuchen Li
Zeyi Sun
Jundong Li
XAI
372
13
0
09 Jan 2024
Dense Hopfield Networks in the Teacher-Student Setting
Dense Hopfield Networks in the Teacher-Student SettingSciPost Physics (SciPost Phys.), 2024
Robin Thériault
Daniele Tantari
AAML
264
9
0
08 Jan 2024
Data-Dependent Stability Analysis of Adversarial Training
Data-Dependent Stability Analysis of Adversarial Training
Yihan Wang
Shuang Liu
Xiao-Shan Gao
248
6
0
06 Jan 2024
Fair Sampling in Diffusion Models through Switching Mechanism
Fair Sampling in Diffusion Models through Switching Mechanism
Yujin Choi
Jinseong Park
Hoki Kim
Jaewook Lee
Saeroom Park
DiffM
245
14
0
06 Jan 2024
Null Space Properties of Neural Networks with Applications to Image
  Steganography
Null Space Properties of Neural Networks with Applications to Image Steganography
Xiang Li
Kevin M. Short
AAML
139
1
0
01 Jan 2024
Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
Haz Sameen Shahgir
Xianghao Kong
Greg Ver Steeg
Yue Dong
315
6
0
22 Dec 2023
Where and How to Attack? A Causality-Inspired Recipe for Generating
  Counterfactual Adversarial Examples
Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples
Ruichu Cai
Yuxuan Zhu
Jie Qiao
Zefeng Liang
Furui Liu
Zhifeng Hao
CML
384
5
0
21 Dec 2023
Fragility, Robustness and Antifragility in Deep Learning
Fragility, Robustness and Antifragility in Deep LearningArtificial Intelligence (AIJ), 2023
Chandresh Pravin
Ivan Martino
Giuseppe Nicosia
Varun Ojha
276
5
0
15 Dec 2023
Adaptive Shortcut Debiasing for Online Continual Learning
Adaptive Shortcut Debiasing for Online Continual LearningAAAI Conference on Artificial Intelligence (AAAI), 2023
Doyoung Kim
Dongmin Park
Yooju Shin
Jihwan Bang
Hwanjun Song
Jae-Gil Lee
CLL
225
5
0
14 Dec 2023
Robust Few-Shot Named Entity Recognition with Boundary Discrimination
  and Correlation Purification
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationAAAI Conference on Artificial Intelligence (AAAI), 2023
Xiaojun Xue
Chunxia Zhang
Tianxiang Xu
Zhendong Niu
215
5
0
13 Dec 2023
Artificial Neural Nets and the Representation of Human Concepts
Artificial Neural Nets and the Representation of Human Concepts
Timo Freiesleben
NAI
345
4
0
08 Dec 2023
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
616
8
0
08 Dec 2023
SoK: Unintended Interactions among Machine Learning Defenses and Risks
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu
S. Szyller
Nadarajah Asokan
AAML
380
6
0
07 Dec 2023
Scaling Laws for Adversarial Attacks on Language Model Activations
Scaling Laws for Adversarial Attacks on Language Model Activations
Stanislav Fort
143
21
0
05 Dec 2023
Singular Regularization with Information Bottleneck Improves Model's
  Adversarial Robustness
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness
Guanlin Li
Naishan Zheng
Man Zhou
Jie Zhang
Tianwei Zhang
AAML
139
0
0
04 Dec 2023
Rethinking Adversarial Training with Neural Tangent Kernel
Rethinking Adversarial Training with Neural Tangent Kernel
Guanlin Li
Han Qiu
Shangwei Guo
Jiwei Li
Tianwei Zhang
AAML
310
1
0
04 Dec 2023
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
Towards Sample-specific Backdoor Attack with Clean Labels via Attribute TriggerIEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2023
Yiming Li
Mingyan Zhu
Junfeng Guo
Tao Wei
Shu-Tao Xia
Zhan Qin
AAML
390
5
0
03 Dec 2023
Improving Adversarial Transferability via Model Alignment
Improving Adversarial Transferability via Model AlignmentEuropean Conference on Computer Vision (ECCV), 2023
A. Ma
Amir-massoud Farahmand
Yangchen Pan
Juil Sock
Jindong Gu
AAML
384
9
0
30 Nov 2023
GSE: Group-wise Sparse and Explainable Adversarial Attacks
GSE: Group-wise Sparse and Explainable Adversarial AttacksInternational Conference on Learning Representations (ICLR), 2023
Shpresim Sadiku
Moritz Wagner
Sebastian Pokutta
AAML
366
4
0
29 Nov 2023
Adversarial Purification of Information Masking
Adversarial Purification of Information Masking
Sitong Liu
Z. Lian
Shuangquan Zhang
Liang Xiao
AAML
205
1
0
26 Nov 2023
Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using
  Adversarial Training
Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using Adversarial Training
Karina Yang
Alexis Bennett
Dominique Duncan
OOD
211
2
0
23 Nov 2023
Adversarial defense based on distribution transfer
Adversarial defense based on distribution transfer
Jiahao Chen
Diqun Yan
Li Dong
187
0
0
23 Nov 2023
Panda or not Panda? Understanding Adversarial Attacks with Interactive
  Visualization
Panda or not Panda? Understanding Adversarial Attacks with Interactive Visualization
Yuzhe You
Jarvis Tse
Jian Zhao
AAML
142
4
0
22 Nov 2023
On The Relationship Between Universal Adversarial Attacks And Sparse
  Representations
On The Relationship Between Universal Adversarial Attacks And Sparse RepresentationsIEEE Open Journal of Signal Processing (IEEE Open J. Signal Process.), 2023
Dana Weitzner
Raja Giryes
AAML
281
0
0
14 Nov 2023
Towards Improving Robustness Against Common Corruptions in Object
  Detectors Using Adversarial Contrastive Learning
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning
Shashank Kotyan
Danilo Vasconcellos Vargas
AAML
207
0
0
14 Nov 2023
Examining Common Paradigms in Multi-Task Learning
Examining Common Paradigms in Multi-Task Learning
Cathrin Elich
Lukas Kirchdorfer
Jan M. Kohler
Lukas Schott
286
3
0
08 Nov 2023
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from
  a Minimax Game Perspective
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game PerspectiveNeural Information Processing Systems (NeurIPS), 2023
Yifei Wang
Liangchen Li
Jiansheng Yang
Zhouchen Lin
Yisen Wang
283
19
0
30 Oct 2023
Blacksmith: Fast Adversarial Training of Vision Transformers via a
  Mixture of Single-step and Multi-step Methods
Blacksmith: Fast Adversarial Training of Vision Transformers via a Mixture of Single-step and Multi-step Methods
Mahdi Salmani
Alireza Dehghanpour Farashah
Mohammad Azizmalayeri
Mahdi Amiri
Navid Eslami
M. T. Manzuri
M. Rohban
AAML
166
1
0
29 Oct 2023
Adversarial Examples Are Not Real Features
Adversarial Examples Are Not Real FeaturesNeural Information Processing Systems (NeurIPS), 2023
Ang Li
Yifei Wang
Yiwen Guo
Yisen Wang
633
20
0
29 Oct 2023
Label Poisoning is All You Need
Label Poisoning is All You NeedNeural Information Processing Systems (NeurIPS), 2023
Rishi Jha
J. Hayase
Sewoong Oh
AAML
263
44
0
29 Oct 2023
Understanding and Improving Ensemble Adversarial Defense
Understanding and Improving Ensemble Adversarial DefenseNeural Information Processing Systems (NeurIPS), 2023
Yian Deng
Tingting Mu
AAML
324
28
0
27 Oct 2023
A Survey on Transferability of Adversarial Examples across Deep Neural
  Networks
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
Jindong Gu
Yang Liu
Pau de Jorge
Wenqain Yu
Xinwei Liu
...
Anjun Hu
Ashkan Khakzar
Zhijiang Li
Simeng Qin
Juil Sock
AAML
397
50
0
26 Oct 2023
Instability of computer vision models is a necessary result of the task
  itself
Instability of computer vision models is a necessary result of the task itself
Oliver Turnbull
G. Cevora
AAML
64
1
0
26 Oct 2023
Data Optimization in Deep Learning: A Survey
Data Optimization in Deep Learning: A SurveyIEEE Transactions on Knowledge and Data Engineering (TKDE), 2023
Ou Wu
Rujing Yao
330
6
0
25 Oct 2023
Machine Learning and Knowledge: Why Robustness Matters
Machine Learning and Knowledge: Why Robustness Matters
Jonathan Vandenburgh
OOD
236
4
0
23 Oct 2023
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