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On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
v1v2v3 (latest)

On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning

7 October 2024
Yongyi Su
Yushu Li
Nanqing Liu
Kui Jia
Xulei Yang
Chuan-Sheng Foo
Xun Xu
    TTAAAML
ArXiv (abs)PDFHTML

Papers citing "On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning"

50 / 56 papers shown
Title
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
Nanqing Liu
Xun Xu
Yongyi Su
Haojie Zhang
Heng-Chao Li
VLM
115
15
0
20 Sep 2024
Distribution Alignment for Fully Test-Time Adaptation with Dynamic
  Online Data Streams
Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams
Ziqiang Wang
Zhixiang Chi
Yanan Wu
Li Gu
Zhi Liu
Konstantinos Plataniotis
Yang Wang
OODTTA
72
4
0
16 Jul 2024
Adapting to Distribution Shift by Visual Domain Prompt Generation
Adapting to Distribution Shift by Visual Domain Prompt Generation
Zhixiang Chi
Li Gu
Tao Zhong
Huan Liu
Yuanhao Yu
Konstantinos N Plataniotis
Yang Wang
VLMOOD
91
10
0
05 May 2024
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
Hyejin Park
Jeongyeon Hwang
Sunung Mun
Sangdon Park
Jungseul Ok
AAMLTTAOOD
80
6
0
28 Mar 2024
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Yanan Wu
Zhixiang Chi
Yang Wang
Konstantinos N. Plataniotis
Songhe Feng
OOD
81
20
0
15 Dec 2023
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
Yongyi Su
Xun Xu
Kui Jia
TTA
154
26
0
26 Sep 2023
On the Robustness of Open-World Test-Time Training: Self-Training with
  Dynamic Prototype Expansion
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
Yu-Sheng Li
Xun Xu
Yongyi Su
Kui Jia
OODVLMTTA
65
22
0
19 Aug 2023
Test-Time Poisoning Attacks Against Test-Time Adaptation Models
Test-Time Poisoning Attacks Against Test-Time Adaptation Models
Tianshuo Cong
Xinlei He
Yun Shen
Yang Zhang
AAMLTTA
59
6
0
16 Aug 2023
Universal Test-time Adaptation through Weight Ensembling, Diversity
  Weighting, and Prior Correction
Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction
Robert A. Marsden
Mario Döbler
Bin Yang
TTA
84
38
0
01 Jun 2023
DINOv2: Learning Robust Visual Features without Supervision
DINOv2: Learning Robust Visual Features without Supervision
Maxime Oquab
Timothée Darcet
Théo Moutakanni
Huy Q. Vo
Marc Szafraniec
...
Hervé Jégou
Julien Mairal
Patrick Labatut
Armand Joulin
Piotr Bojanowski
VLMCLIPSSL
399
3,514
0
14 Apr 2023
Segment Anything
Segment Anything
A. Kirillov
Eric Mintun
Nikhila Ravi
Hanzi Mao
Chloe Rolland
...
Spencer Whitehead
Alexander C. Berg
Wan-Yen Lo
Piotr Dollár
Ross B. Girshick
MLLMVLM
397
7,421
0
05 Apr 2023
STFAR: Improving Object Detection Robustness at Test-Time by
  Self-Training with Feature Alignment Regularization
STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization
Yijin Chen
Xun Xu
Yongyi Su
Kui Jia
TTAOODObjD
107
6
0
31 Mar 2023
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
Jian Liang
Ran He
Tien-Ping Tan
OODVLMTTA
133
243
0
27 Mar 2023
Robust Test-Time Adaptation in Dynamic Scenarios
Robust Test-Time Adaptation in Dynamic Scenarios
Longhui Yuan
Binhui Xie
Shuangliang Li
TTA
96
126
0
24 Mar 2023
Revisiting Realistic Test-Time Training: Sequential Inference and
  Adaptation by Anchored Clustering Regularized Self-Training
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training
Yongyi Su
Xun Xu
Tianrui Li
Kui Jia
OODTTA
68
17
0
20 Mar 2023
EcoTTA: Memory-Efficient Continual Test-time Adaptation via
  Self-distilled Regularization
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
Jun S. Song
Jungsoo Lee
In So Kweon
Sungha Choi
TTA
95
94
0
03 Mar 2023
Towards Stable Test-Time Adaptation in Dynamic Wild World
Towards Stable Test-Time Adaptation in Dynamic Wild World
Shuaicheng Niu
Jiaxiang Wu
Yifan Zhang
Z. Wen
Yaofo Chen
P. Zhao
Mingkui Tan
TTA
108
280
0
24 Feb 2023
Uncovering Adversarial Risks of Test-Time Adaptation
Uncovering Adversarial Risks of Test-Time Adaptation
Tong Wu
Feiran Jia
Xiangyu Qi
Jiachen T. Wang
Vikash Sehwag
Saeed Mahloujifar
Prateek Mittal
AAMLTTA
111
9
0
29 Jan 2023
A Probabilistic Framework for Lifelong Test-Time Adaptation
A Probabilistic Framework for Lifelong Test-Time Adaptation
Dhanajit Brahma
Piyush Rai
TTA
68
36
0
19 Dec 2022
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation
Mario Döbler
Robert A. Marsden
Bin Yang
OODTTA
74
90
0
23 Nov 2022
Visual Prompt Tuning for Test-time Domain Adaptation
Visual Prompt Tuning for Test-time Domain Adaptation
Yunhe Gao
Xingjian Shi
Yi Zhu
Hongya Wang
Zhiqiang Tang
Xiong Zhou
Mu Li
Dimitris N. Metaxas
VPVLMVLM
165
89
0
10 Oct 2022
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from
  Mixture-of-Experts
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Tao Zhong
Zhixiang Chi
Li Gu
Yang Wang
Yuanhao Yu
Jingshan Tang
OOD
143
33
0
08 Oct 2022
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
Taesik Gong
Jongheon Jeong
Taewon Kim
Yewon Kim
Jinwoo Shin
Sung-Ju Lee
OODTTA
118
131
0
10 Aug 2022
Revisiting Realistic Test-Time Training: Sequential Inference and
  Adaptation by Anchored Clustering
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
Yongyi Su
Xun Xu
Kui Jia
TTAOOD
88
46
0
06 Jun 2022
Efficient Test-Time Model Adaptation without Forgetting
Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu
Jiaxiang Wu
Yifan Zhang
Yaofo Chen
S. Zheng
P. Zhao
Mingkui Tan
OODVLMTTA
96
351
0
06 Apr 2022
Continual Test-Time Domain Adaptation
Continual Test-Time Domain Adaptation
Qin Wang
Olga Fink
Luc Van Gool
Dengxin Dai
OODTTA
114
432
0
25 Mar 2022
Towards Evaluating the Robustness of Neural Networks Learned by
  Transduction
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Jiefeng Chen
Xi Wu
Yang Guo
Yingyu Liang
S. Jha
ELMAAML
74
15
0
27 Oct 2021
MEMO: Test Time Robustness via Adaptation and Augmentation
MEMO: Test Time Robustness via Adaptation and Augmentation
Marvin Zhang
Sergey Levine
Chelsea Finn
OODTTA
135
328
0
18 Oct 2021
Adversarial Examples Make Strong Poisons
Adversarial Examples Make Strong Poisons
Liam H. Fowl
Micah Goldblum
Ping Yeh-Chiang
Jonas Geiping
Wojtek Czaja
Tom Goldstein
SILM
105
136
0
21 Jun 2021
Black-box adversarial attacks using Evolution Strategies
Black-box adversarial attacks using Evolution Strategies
Hao Qiu
Leonardo Lucio Custode
Giovanni Iacca
AAML
70
18
0
30 Apr 2021
If your data distribution shifts, use self-learning
If your data distribution shifts, use self-learning
E. Rusak
Steffen Schneider
George Pachitariu
L. Eck
Peter V. Gehler
Oliver Bringmann
Wieland Brendel
Matthias Bethge
VLMOODTTA
152
33
0
27 Apr 2021
Grey-box Adversarial Attack And Defence For Sentiment Classification
Grey-box Adversarial Attack And Defence For Sentiment Classification
Ying Xu
Xu Zhong
Antonio Jimeno Yepes
Jey Han Lau
VLMAAML
64
54
0
22 Mar 2021
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Alec Radford
Jong Wook Kim
Chris Hallacy
Aditya A. Ramesh
Gabriel Goh
...
Amanda Askell
Pamela Mishkin
Jack Clark
Gretchen Krueger
Ilya Sutskever
CLIPVLM
1.0K
29,926
0
26 Feb 2021
Unlearnable Examples: Making Personal Data Unexploitable
Unlearnable Examples: Making Personal Data Unexploitable
Hanxun Huang
Xingjun Ma
S. Erfani
James Bailey
Yisen Wang
MIACV
249
194
0
13 Jan 2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at
  Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy
Lucas Beyer
Alexander Kolesnikov
Dirk Weissenborn
Xiaohua Zhai
...
Matthias Minderer
G. Heigold
Sylvain Gelly
Jakob Uszkoreit
N. Houlsby
ViT
684
41,563
0
22 Oct 2020
QEBA: Query-Efficient Boundary-Based Blackbox Attack
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Huichen Li
Xiaojun Xu
Xiaolu Zhang
Shuang Yang
Yue Liu
AAML
123
183
0
28 May 2020
Reliable evaluation of adversarial robustness with an ensemble of
  diverse parameter-free attacks
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce
Matthias Hein
AAML
241
1,861
0
03 Mar 2020
Do We Really Need to Access the Source Data? Source Hypothesis Transfer
  for Unsupervised Domain Adaptation
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Jian Liang
Dapeng Hu
Jiashi Feng
138
1,251
0
20 Feb 2020
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
Eric Arazo
Diego Ortego
Paul Albert
Noel E. O'Connor
Kevin McGuinness
123
846
0
08 Aug 2019
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via
  Genetic Algorithm
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
Jinyin Chen
Mengmeng Su
Shijing Shen
Hui Xiong
Haibin Zheng
AAML
124
68
0
01 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and
  Perturbations
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks
Thomas G. Dietterich
OODVLM
198
3,458
0
28 Mar 2019
Adversarial Attacks and Defences: A Survey
Adversarial Attacks and Defences: A Survey
Anirban Chakraborty
Manaar Alam
Vishal Dey
Anupam Chattopadhyay
Debdeep Mukhopadhyay
AAMLOOD
92
683
0
28 Sep 2018
Black-box Adversarial Attacks with Limited Queries and Information
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas
Logan Engstrom
Anish Athalye
Jessy Lin
MLAUAAML
170
1,208
0
23 Apr 2018
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi
Wenjie Huang
Mahyar Najibi
Octavian Suciu
Christoph Studer
Tudor Dumitras
Tom Goldstein
AAML
91
1,097
0
03 Apr 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A
  Survey
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar
Ajmal Mian
AAML
112
1,871
0
02 Jan 2018
Evasion Attacks against Machine Learning at Test Time
Evasion Attacks against Machine Learning at Test Time
Battista Biggio
Igino Corona
Davide Maiorca
B. Nelson
Nedim Srndic
Pavel Laskov
Giorgio Giacinto
Fabio Roli
AAML
168
2,160
0
21 Aug 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural
  Networks without Training Substitute Models
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Pin-Yu Chen
Huan Zhang
Yash Sharma
Jinfeng Yi
Cho-Jui Hsieh
AAML
108
1,887
0
14 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
323
12,151
0
19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses
Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr
Alexey Kurakin
Nicolas Papernot
Ian Goodfellow
Dan Boneh
Patrick McDaniel
AAML
187
2,731
0
19 May 2017
Generative Poisoning Attack Method Against Neural Networks
Generative Poisoning Attack Method Against Neural Networks
Chaofei Yang
Qing Wu
Hai Helen Li
Yiran Chen
AAML
76
218
0
03 Mar 2017
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