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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
Cross-Modal Conceptualization in Bottleneck Models
Cross-Modal Conceptualization in Bottleneck ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Danis Alukaev
S. Kiselev
Ilya Pershin
Bulat Ibragimov
Vladimir Ivanov
Alexey Kornaev
Ivan Titov
268
9
0
23 Oct 2023
Survey of Vulnerabilities in Large Language Models Revealed by
  Adversarial Attacks
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks
Erfan Shayegani
Md Abdullah Al Mamun
Yu Fu
Pedram Zaree
Yue Dong
Nael B. Abu-Ghazaleh
AAML
476
230
0
16 Oct 2023
Regularization properties of adversarially-trained linear regression
Regularization properties of adversarially-trained linear regressionNeural Information Processing Systems (NeurIPS), 2023
Antônio H. Ribeiro
Dave Zachariah
Francis Bach
Thomas B. Schön
AAML
268
19
0
16 Oct 2023
Black-box Targeted Adversarial Attack on Segment Anything (SAM)
Black-box Targeted Adversarial Attack on Segment Anything (SAM)
Sheng Zheng
Chaoning Zhang
Xinhong Hao
AAML
406
12
0
16 Oct 2023
Is Certifying $\ell_p$ Robustness Still Worthwhile?
Is Certifying ℓp\ell_pℓp​ Robustness Still Worthwhile?
Ravi Mangal
Klas Leino
Zifan Wang
Kai Hu
Weicheng Yu
Corina S. Pasareanu
Anupam Datta
Matt Fredrikson
AAMLOOD
250
1
0
13 Oct 2023
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced
  Transfer Learning
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer LearningNeural Information Processing Systems (NeurIPS), 2023
Yihua Zhang
Yimeng Zhang
Chenyi Zi
Jinghan Jia
Jiancheng Liu
Gaowen Liu
Min-Fong Hong
Shiyu Chang
Sijia Liu
AAML
383
14
0
13 Oct 2023
Does resistance to style-transfer equal Global Shape Bias? Measuring
  network sensitivity to global shape configuration
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configuration
Ziqi Wen
Tianqin Li
Zhi Jing
Tai Sing Lee
OOD
343
1
0
11 Oct 2023
GraphCloak: Safeguarding Task-specific Knowledge within Graph-structured
  Data from Unauthorized Exploitation
GraphCloak: Safeguarding Task-specific Knowledge within Graph-structured Data from Unauthorized Exploitation
Yixin Liu
Chenrui Fan
Xun Chen
Pan Zhou
Lichao Sun
217
4
0
11 Oct 2023
Investigating the Adversarial Robustness of Density Estimation Using the
  Probability Flow ODE
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Marius Arvinte
Cory Cornelius
Jason Martin
N. Himayat
DiffM
294
5
0
10 Oct 2023
AttributionLab: Faithfulness of Feature Attribution Under Controllable
  Environments
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
Yang Zhang
Yawei Li
Hannah Brown
Mina Rezaei
B. Bischl
Juil Sock
Ashkan Khakzar
Kenji Kawaguchi
OOD
261
3
0
10 Oct 2023
Understanding the Robustness of Multi-modal Contrastive Learning to
  Distribution Shift
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution ShiftInternational Conference on Learning Representations (ICLR), 2023
Yihao Xue
Siddharth Joshi
Dang Nguyen
Baharan Mirzasoleiman
VLM
249
5
0
08 Oct 2023
Robustness-enhanced Uplift Modeling with Adversarial Feature
  Desensitization
Robustness-enhanced Uplift Modeling with Adversarial Feature DesensitizationIndustrial Conference on Data Mining (IDM), 2023
Zexu Sun
Bowei He
Ming Ma
Jiakai Tang
Yuchen Wang
Chen Ma
Dugang Liu
340
6
0
07 Oct 2023
Generating Less Certain Adversarial Examples Improves Robust Generalization
Generating Less Certain Adversarial Examples Improves Robust Generalization
Minxing Zhang
Michael Backes
Xiao Zhang
AAML
557
1
0
06 Oct 2023
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial
  Examples
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
Jia-Yu Yao
Hai-Jian Ke
Zhen-Hui Liu
Munan Ning
Li Yuan
HILMLRMAAML
398
271
0
02 Oct 2023
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models
  Against Adversarial Attacks
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial AttacksACM Computing Surveys (ACM Comput. Surv.), 2023
Yanjie Li
Bin Xie
Songtao Guo
Yuanyuan Yang
Bin Xiao
AAML
273
36
0
01 Oct 2023
On Continuity of Robust and Accurate Classifiers
On Continuity of Robust and Accurate Classifiers
Ramin Barati
Reza Safabakhsh
Mohammad Rahmati
AAML
368
1
0
29 Sep 2023
Investigating Human-Identifiable Features Hidden in Adversarial
  Perturbations
Investigating Human-Identifiable Features Hidden in Adversarial Perturbations
Dennis Y. Menn
Tzu-hsun Feng
Sriram Vishwanath
Hung-yi Lee
AAML
171
0
0
28 Sep 2023
On the Computational Entanglement of Distant Features in Adversarial
  Machine Learning
On the Computational Entanglement of Distant Features in Adversarial Machine Learning
Yen-Lung Lai
Xingbo Dong
Zhe Jin
AAML
468
0
0
27 Sep 2023
Improving Robustness of Deep Convolutional Neural Networks via
  Multiresolution Learning
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning
Hongyan Zhou
Yao Liang
OOD
272
0
0
24 Sep 2023
Toward a Deeper Understanding: RetNet Viewed through Convolution
Toward a Deeper Understanding: RetNet Viewed through ConvolutionPattern Recognition (Pattern Recogn.), 2023
Chenghao Li
Chaoning Zhang
ViT
256
16
0
11 Sep 2023
Good-looking but Lacking Faithfulness: Understanding Local Explanation
  Methods through Trend-based Testing
Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based TestingConference on Computer and Communications Security (CCS), 2023
Jinwen He
Kai Chen
Guozhu Meng
Jiangshan Zhang
Congyi Li
FAttAAML
263
4
0
09 Sep 2023
Exploring Robust Features for Improving Adversarial Robustness
Exploring Robust Features for Improving Adversarial RobustnessIEEE Transactions on Cybernetics (IEEE Trans. Cybern.), 2023
Hong Wang
Yuefan Deng
Shinjae Yoo
Lu Ma
AAML
337
5
0
09 Sep 2023
Robust Adversarial Defense by Tensor Factorization
Robust Adversarial Defense by Tensor FactorizationInternational Conference on Machine Learning and Applications (ICMLA), 2023
Manish Bhattarai
M. C. Kaymak
Ryan Barron
Ben Nebgen
Kim Ø. Rasmussen
Boian Alexandrov
AAML
184
2
0
03 Sep 2023
Why do universal adversarial attacks work on large language models?:
  Geometry might be the answer
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Varshini Subhash
Anna Bialas
Weiwei Pan
Finale Doshi-Velez
AAML
215
16
0
01 Sep 2023
Adversarial Finetuning with Latent Representation Constraint to Mitigate
  Accuracy-Robustness Tradeoff
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness TradeoffIEEE International Conference on Computer Vision (ICCV), 2023
Satoshi Suzuki
Shin'ya Yamaguchi
Shoichiro Takeda
Sekitoshi Kanai
Naoki Makishima
Atsushi Ando
Ryo Masumura
AAML
274
7
0
31 Aug 2023
Intriguing Properties of Diffusion Models: An Empirical Study of the
  Natural Attack Capability in Text-to-Image Generative Models
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative ModelsComputer Vision and Pattern Recognition (CVPR), 2023
Takami Sato
Justin Yue
Nanze Chen
Ningfei Wang
Qi Alfred Chen
DiffM
226
7
0
30 Aug 2023
Advancing Adversarial Robustness Through Adversarial Logit Update
Advancing Adversarial Robustness Through Adversarial Logit Update
Hao Xuan
Peican Zhu
Xingyu Li
AAML
275
0
0
29 Aug 2023
On-Manifold Projected Gradient Descent
On-Manifold Projected Gradient Descent
Aaron Mahler
Tyrus Berry
Thomas Stephens
Harbir Antil
Michael Merritt
Jeanie Schreiber
Ioannis G. Kevrekidis
AAML
218
0
0
23 Aug 2023
RemovalNet: DNN Fingerprint Removal Attacks
RemovalNet: DNN Fingerprint Removal AttacksIEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2023
Hongwei Yao
Zhengguang Li
Kunzhe Huang
Jian Lou
Zhan Qin
Kui Ren
MLAUAAML
260
4
0
23 Aug 2023
Adversarial Illusions in Multi-Modal Embeddings
Adversarial Illusions in Multi-Modal EmbeddingsUSENIX Security Symposium (USENIX Security), 2023
Tingwei Zhang
Rishi Jha
Eugene Bagdasaryan
Vitaly Shmatikov
AAML
805
28
0
22 Aug 2023
Spurious Correlations and Where to Find Them
Spurious Correlations and Where to Find Them
Gautam Sreekumar
Vishnu Boddeti
CML
222
7
0
21 Aug 2023
Measuring the Effect of Causal Disentanglement on the Adversarial
  Robustness of Neural Network Models
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network ModelsInternational Conference on Information and Knowledge Management (CIKM), 2023
Preben Ness
D. Marijan
Sunanda Bose
CML
171
0
0
21 Aug 2023
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Hejia Geng
Peng Li
AAML
413
4
0
20 Aug 2023
Backdoor Mitigation by Correcting the Distribution of Neural Activations
Backdoor Mitigation by Correcting the Distribution of Neural Activations
Xi Li
Zhen Xiang
David J. Miller
G. Kesidis
AAML
162
0
0
18 Aug 2023
Balancing Transparency and Risk: The Security and Privacy Risks of
  Open-Source Machine Learning Models
Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models
Dominik Hintersdorf
Lukas Struppek
Kristian Kersting
SILM
162
6
0
18 Aug 2023
Not So Robust After All: Evaluating the Robustness of Deep Neural
  Networks to Unseen Adversarial Attacks
Not So Robust After All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks
R. Garaev
Bader Rasheed
Adil Mehmood Khan
AAMLOOD
79
3
0
12 Aug 2023
Fixed Inter-Neuron Covariability Induces Adversarial Robustness
Fixed Inter-Neuron Covariability Induces Adversarial RobustnessIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
Muhammad Ahmed Shah
Bhiksha Raj
AAML
99
0
0
07 Aug 2023
TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal
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TIJO: Trigger Inversion with Joint Optimization for Defending Multimodal Backdoored ModelsIEEE International Conference on Computer Vision (ICCV), 2023
Indranil Sur
Karan Sikka
Matthew Walmer
K. Koneripalli
Anirban Roy
Xiaoyu Lin
Ajay Divakaran
Susmit Jha
156
12
0
07 Aug 2023
A reading survey on adversarial machine learning: Adversarial attacks
  and their understanding
A reading survey on adversarial machine learning: Adversarial attacks and their understanding
Shashank Kotyan
AAML
169
11
0
07 Aug 2023
Unsupervised Adversarial Detection without Extra Model: Training Loss
  Should Change
Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change
Chien Cheng Chyou
Hung-Ting Su
Winston H. Hsu
AAML
94
3
0
07 Aug 2023
FROD: Robust Object Detection for Free
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Muhammad Awais
Awais
Weiming Zhuang
Zhuang
Lingjuan
Lingjuan Lyu
Sung-Ho
Sung-Ho Bae
ObjD
185
2
0
03 Aug 2023
Training on Foveated Images Improves Robustness to Adversarial Attacks
Training on Foveated Images Improves Robustness to Adversarial AttacksNeural Information Processing Systems (NeurIPS), 2023
Muhammad Ahmed Shah
Bhiksha Raj
AAML
208
6
0
01 Aug 2023
An Exact Kernel Equivalence for Finite Classification Models
An Exact Kernel Equivalence for Finite Classification Models
Brian Bell
Michaela Geyer
David Glickenstein
Amanda Fernandez
Juston Moore
280
4
0
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Transferable Attack for Semantic Segmentation
Transferable Attack for Semantic Segmentation
Mengqi He
Jing Zhang
Zhaoyuan Yang
Mingyi He
Nick Barnes
Yuchao Dai
226
2
0
31 Jul 2023
Universal and Transferable Adversarial Attacks on Aligned Language
  Models
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Andy Zou
Zifan Wang
Nicholas Carlini
Milad Nasr
J. Zico Kolter
Matt Fredrikson
647
2,367
0
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NSA: Naturalistic Support Artifact to Boost Network Confidence
NSA: Naturalistic Support Artifact to Boost Network ConfidenceIEEE International Joint Conference on Neural Network (IJCNN), 2023
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Phil Munz
Apurva Narayan
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212
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Towards Generic and Controllable Attacks Against Object Detection
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Fast Adaptive Test-Time Defense with Robust Features
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Anurag Singh
Mahalakshmi Sabanayagam
Krikamol Muandet
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185
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A Holistic Assessment of the Reliability of Machine Learning Systems
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Anthony Corso
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316
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On the Robustness of Split Learning against Adversarial Attacks
On the Robustness of Split Learning against Adversarial AttacksEuropean Conference on Artificial Intelligence (ECAI), 2023
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171
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0
16 Jul 2023
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