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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
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
Zesheng Ye
C. Cai
Ruijiang Dong
Jianzhong Qi
Bingquan Shen
Pin-Yu Chen
Feng Liu
655
1
0
05 Jun 2025
Identifying and Understanding Cross-Class Features in Adversarial Training
Zeming Wei
Yiwen Guo
Yisen Wang
AAML
280
1
0
05 Jun 2025
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Jiawen Zhang
Zhenwei Zhang
Shun Zheng
Xumeng Wen
Jia Li
Jiang Bian
AI4TSAAML
411
1
0
26 May 2025
Ignition Phase : Standard Training for Fast Adversarial Robustness
Ignition Phase : Standard Training for Fast Adversarial Robustness
Wang Yu-Hang
Liu ying
Fang liang
Wang Xuelin
Junkang Guo
Shiwei Li
Lei Gao
Jian Liu
Wenfei Yin
AAML
109
0
0
25 May 2025
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks
Abir Ray
204
0
0
24 May 2025
Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners
Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners
Soichiro Kumano
Hiroshi Kera
Toshihiko Yamasaki
AAML
549
1
0
20 May 2025
Use as Many Surrogates as You Want: Selective Ensemble Attack to Unleash Transferability without Sacrificing Resource Efficiency
Use as Many Surrogates as You Want: Selective Ensemble Attack to Unleash Transferability without Sacrificing Resource Efficiency
Bo Yang
Hengwei Zhang
Jindong Wang
Yuchen Ren
Chenhao Lin
Chao Shen
Subrat Kishore Dutta
AAML
417
0
0
19 May 2025
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP
Hanxun Huang
Sarah Monazam Erfani
Yige Li
Jiabo He
James Bailey
AAML
474
9
0
08 May 2025
Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks
Reliable Disentanglement Multi-view Learning Against View Adversarial AttacksInternational Joint Conference on Artificial Intelligence (IJCAI), 2025
Xuyang Wang
Siyuan Duan
Qizhi Li
Guiduo Duan
Yuan Sun
Dezhong Peng
AAMLEDL
614
1
0
07 May 2025
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i
Kola Ayonrinde
Louis Jaburi
MILM
500
4
0
01 May 2025
A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
Greg Gluch
Shafi Goldwasser
AAML
459
0
0
28 Apr 2025
Representation Learning on a Random Lattice
Representation Learning on a Random Lattice
Aryeh Brill
OODFAttAI4CE
282
0
0
28 Apr 2025
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers
Elad Sofer
Tomer Shaked
Caroline Chaux
Stefano Rini
AAML
293
3
0
26 Apr 2025
Evaluating Uncertainty in Deep Gaussian Processes
Evaluating Uncertainty in Deep Gaussian Processes
Matthijs van der Lende
Jeremias Lino Ferrao
Niclas Müller-Hof
UQCV
235
1
0
24 Apr 2025
Statistical Runtime Verification for LLMs via Robustness Estimation
Statistical Runtime Verification for LLMs via Robustness EstimationRuntime Verification (RV), 2025
Natan Levy
Adiel Ashrov
Guy Katz
AAML
381
0
0
24 Apr 2025
Human Aligned Compression for Robust Models
Human Aligned Compression for Robust Models
Samuel Räber
Andreas Plesner
Till Aczél
Roger Wattenhofer
AAML
337
1
0
16 Apr 2025
Robust SAM: On the Adversarial Robustness of Vision Foundation Models
Robust SAM: On the Adversarial Robustness of Vision Foundation ModelsAAAI Conference on Artificial Intelligence (AAAI), 2025
Jiahuan Long
Zhengqin Xu
Tingsong Jiang
Wen Yao
Shuai Jia
Chao Ma
Xiaoqian Chen
AAMLVLM
303
3
0
11 Apr 2025
Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models
Alireza Aghabagherloo
Aydin Abadi
Sumanta Sarkar
Vishnu Asutosh Dasu
Bart Preneel
AAML
391
5
0
01 Apr 2025
From Colors to Classes: Emergence of Concepts in Vision Transformers
From Colors to Classes: Emergence of Concepts in Vision Transformers
Teresa Dorszewski
Lenka Tětková
Robert Jenssen
Lars Kai Hansen
Kristoffer Wickstrøm
224
11
0
31 Mar 2025
Do regularization methods for shortcut mitigation work as intended?
Do regularization methods for shortcut mitigation work as intended?International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Haoyang Hong
Ioanna Papanikolaou
Sonali Parbhoo
311
3
0
21 Mar 2025
On the Robustness Tradeoff in Fine-Tuning
On the Robustness Tradeoff in Fine-Tuning
Kunyang Li
Jean-Charles Noirot Ferrand
Ryan Sheatsley
Blaine Hoak
Yohan Beugin
Eric Pauley
Patrick McDaniel
274
1
0
19 Mar 2025
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AIIEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2025
Ripan Kumar Kundu
Matthew Denton
Genova Mongalo
Prasad Calyam
K. A. Hoque
AAML
189
1
0
17 Mar 2025
Robust Dataset Distillation by Matching Adversarial Trajectories
Robust Dataset Distillation by Matching Adversarial Trajectories
Wei Lai
Tianyu Ding
ren dongdong
Lei Wang
Jing Huo
Yang Gao
Wenbin Li
AAMLDD
288
1
0
15 Mar 2025
Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data
Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised DataComputer Vision and Pattern Recognition (CVPR), 2025
Lilin Zhang
Chengpei Wu
Ning Yang
367
0
0
14 Mar 2025
MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents
MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents
Lukas Aichberger
Alasdair Paren
Guohao Li
Juil Sock
Y. Gal
Adel Bibi
AAML
341
10
0
13 Mar 2025
Bringing Comparative Cognition To Computers
Konstantinos Voudouris
Lucy G. Cheke
Eric Schulz
ELM
248
0
0
04 Mar 2025
A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice
Eric Heim
Oren Wright
David Shriver
OODFaML
354
0
0
01 Mar 2025
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin
D. Nguyen
Zerui Tao
Konstantinos Slavakis
Toshihisa Tanaka
Qibin Zhao
AAML
320
1
0
25 Feb 2025
Multi-Target Federated Backdoor Attack Based on Feature Aggregation
Multi-Target Federated Backdoor Attack Based on Feature AggregationPattern Recognition (Pattern Recogn.), 2025
Lingguag Hao
K. Hao
Bing Wei
Xue-song Tang
FedMLAAML
373
0
0
23 Feb 2025
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
Testing the Limits of Fine-Tuning for Improving Visual Cognition in Vision Language Models
Luca M. Schulze Buschoff
Konstantinos Voudouris
Elif Akata
Matthias Bethge
Joshua B. Tenenbaum
Eric Schulz
VLMLRM
444
0
1
21 Feb 2025
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness
Emanuele Ballarin
A. Ansuini
Luca Bortolussi
AAML
759
0
0
20 Feb 2025
Imitation Game for Adversarial Disillusion with Multimodal Generative Chain-of-Thought Role-Play
Imitation Game for Adversarial Disillusion with Multimodal Generative Chain-of-Thought Role-Play
Ching-Chun Chang
Fan-Yun Chen
Shih-Hong Gu
Kai Gao
Hanrui Wang
Isao Echizen
AAML
1.0K
0
0
31 Jan 2025
A margin-based replacement for cross-entropy loss
A margin-based replacement for cross-entropy loss
Michael W. Spratling
Heiko H. Schütt
318
0
0
21 Jan 2025
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
Xiao Yang
Gaolei Li
Jianhua Li
AAMLAI4CE
315
4
0
08 Jan 2025
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-OffsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Kasimir Tanner
Matteo Vilucchio
Bruno Loureiro
Florent Krzakala
AAML
404
4
0
31 Dec 2024
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning
Zhifang Zhang
Shuo He
Bingquan Shen
Bingquan Shen
Lei Feng
AAML
562
4
0
29 Dec 2024
Breaking Barriers in Physical-World Adversarial Examples: Improving
  Robustness and Transferability via Robust Feature
Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust FeatureAAAI Conference on Artificial Intelligence (AAAI), 2024
Yichen Wang
Yuxuan Chou
Ziqi Zhou
Hangtao Zhang
Wei Wan
Shengshan Hu
Minghui Li
AAML
321
14
0
22 Dec 2024
The Vulnerability of Language Model Benchmarks: Do They Accurately
  Reflect True LLM Performance?
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?
Sourav Banerjee
Ayushi Agarwal
Eishkaran Singh
ELM
266
20
0
02 Dec 2024
Adversarial Training in Low-Label Regimes with Margin-Based
  Interpolation
Adversarial Training in Low-Label Regimes with Margin-Based Interpolation
Tian Ye
Rajgopal Kannan
Viktor Prasanna
AAML
256
0
0
27 Nov 2024
Brain-like emergent properties in deep networks: impact of network architecture, datasets and training
Brain-like emergent properties in deep networks: impact of network architecture, datasets and training
Niranjan Rajesh
Georgin Jacob
SP Arun
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386
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Improving Transferable Targeted Attacks with Feature Tuning Mixup
Improving Transferable Targeted Attacks with Feature Tuning MixupComputer Vision and Pattern Recognition (CVPR), 2024
K. Liang
Xuelong Dai
Yanjie Li
Dong Wang
Bin Xiao
AAML
1.2K
4
0
23 Nov 2024
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and AnticipationComputer Vision and Pattern Recognition (CVPR), 2024
Rohith Peddi
Saurabh
Ayush Abhay Shrivastava
Parag Singla
Vibhav Gogate
397
3
0
20 Nov 2024
Computable Model-Independent Bounds for Adversarial Quantum Machine
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Computable Model-Independent Bounds for Adversarial Quantum Machine LearningIEEE Transactions on Quantum Engineering (IEEE Trans. Quantum Eng.), 2024
Bacui Li
T. Alpcan
Chandra Thapa
Udaya Parampalli
AAML
224
0
0
11 Nov 2024
Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models
Layer-wise Alignment: Examining Safety Alignment Across Image Encoder Layers in Vision Language Models
Saketh Bachu
Erfan Shayegani
Trishna Chakraborty
Rohit Lal
Arindam Dutta
Chengyu Song
Yue Dong
Nael B. Abu-Ghazaleh
Amit K. Roy-Chowdhury
278
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Lost in Context: The Influence of Context on Feature Attribution Methods
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Lost in Context: The Influence of Context on Feature Attribution Methods for Object RecognitionIndian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), 2024
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Rishav Kumar
Konda Reddy Mopuri
Rajalakshmi Pachamuthu
240
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Achieving Domain-Independent Certified Robustness via Knowledge
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297
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CausAdv: A Causal-based Framework for Detecting Adversarial Examples
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Props for Machine-Learning Security
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Robust Feature Learning for Multi-Index Models in High Dimensions
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Murat A. Erdogdu
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PEAS: A Strategy for Crafting Transferable Adversarial Examples
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Yisroel Mirsky
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267
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