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Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free

24 May 2022
Tianlong Chen
Zhenyu Zhang
Yihua Zhang
Shiyu Chang
Sijia Liu
Zhangyang Wang
    AAML
ArXiv (abs)PDFHTMLGithub (26★)

Papers citing "Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free"

39 / 39 papers shown
Title
GANs Can Play Lottery Tickets Too
GANs Can Play Lottery Tickets Too
Xuxi Chen
Zhenyu Zhang
Yongduo Sui
Tianlong Chen
GAN
66
58
0
31 May 2021
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks,
  and Defenses
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Micah Goldblum
Dimitris Tsipras
Chulin Xie
Xinyun Chen
Avi Schwarzschild
Basel Alomair
Aleksander Madry
Yue Liu
Tom Goldstein
SILM
102
282
0
18 Dec 2020
The Lottery Tickets Hypothesis for Supervised and Self-supervised
  Pre-training in Computer Vision Models
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
Tianlong Chen
Jonathan Frankle
Shiyu Chang
Sijia Liu
Yang Zhang
Michael Carbin
Zhangyang Wang
66
123
0
12 Dec 2020
Poisoned classifiers are not only backdoored, they are fundamentally
  broken
Poisoned classifiers are not only backdoored, they are fundamentally broken
Mingjie Sun
Siddhant Agarwal
J. Zico Kolter
52
26
0
18 Oct 2020
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu
Xingjun Ma
James Bailey
Feng Lu
AAML
96
516
0
05 Jul 2020
ConFoc: Content-Focus Protection Against Trojan Attacks on Neural
  Networks
ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
Miguel Villarreal-Vasquez
B. Bhargava
AAML
84
38
0
01 Jul 2020
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and
  Data Poisoning Attacks
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
Avi Schwarzschild
Micah Goldblum
Arjun Gupta
John P. Dickerson
Tom Goldstein
AAMLTDI
98
164
0
22 Jun 2020
Rethinking the Trigger of Backdoor Attack
Rethinking the Trigger of Backdoor Attack
Yiming Li
Tongqing Zhai
Baoyuan Wu
Yong Jiang
Zhifeng Li
Shutao Xia
LLMSV
67
150
0
09 Apr 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Alex Renda
Jonathan Frankle
Michael Carbin
283
388
0
05 Mar 2020
Defending against Backdoor Attack on Deep Neural Networks
Defending against Backdoor Attack on Deep Neural Networks
Kaidi Xu
Sijia Liu
Pin-Yu Chen
Pu Zhao
Xinyu Lin
Xue Lin
AAML
68
49
0
26 Feb 2020
Fast is better than free: Revisiting adversarial training
Fast is better than free: Revisiting adversarial training
Eric Wong
Leslie Rice
J. Zico Kolter
AAMLOOD
140
1,181
0
12 Jan 2020
PyHessian: Neural Networks Through the Lens of the Hessian
PyHessian: Neural Networks Through the Lens of the Hessian
Z. Yao
A. Gholami
Kurt Keutzer
Michael W. Mahoney
ODL
69
303
0
16 Dec 2019
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle
Gintare Karolina Dziugaite
Daniel M. Roy
Michael Carbin
MoMe
163
628
0
11 Dec 2019
Hidden Trigger Backdoor Attacks
Hidden Trigger Backdoor Attacks
Aniruddha Saha
Akshayvarun Subramanya
Hamed Pirsiavash
89
627
0
30 Sep 2019
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan
  Backdoors in AI Systems
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems
Wenbo Guo
Lun Wang
Masashi Sugiyama
Min Du
Basel Alomair
73
230
0
02 Aug 2019
Importance Estimation for Neural Network Pruning
Importance Estimation for Neural Network Pruning
Pavlo Molchanov
Arun Mallya
Stephen Tyree
I. Frosio
Jan Kautz
3DPC
81
885
0
25 Jun 2019
Playing the lottery with rewards and multiple languages: lottery tickets
  in RL and NLP
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP
Haonan Yu
Sergey Edunov
Yuandong Tian
Ari S. Morcos
52
150
0
06 Jun 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu
Wenjie Huang
Ali Shafahi
Hengduo Li
Gavin Taylor
Christoph Studer
Tom Goldstein
89
285
0
15 May 2019
The State of Sparsity in Deep Neural Networks
The State of Sparsity in Deep Neural Networks
Trevor Gale
Erich Elsen
Sara Hooker
163
762
0
25 Feb 2019
Model Compression with Adversarial Robustness: A Unified Optimization
  Framework
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Shupeng Gui
Haotao Wang
Chen Yu
Haichuan Yang
Zhangyang Wang
Ji Liu
MQ
59
138
0
10 Feb 2019
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using
  Alternating Direction Method of Multipliers
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
Ao Ren
Tianyun Zhang
Shaokai Ye
Jiayu Li
Wenyao Xu
Xuehai Qian
Xinyu Lin
Yanzhi Wang
MQ
95
161
0
31 Dec 2018
SNIP: Single-shot Network Pruning based on Connection Sensitivity
SNIP: Single-shot Network Pruning based on Connection Sensitivity
Namhoon Lee
Thalaiyasingam Ajanthan
Philip Torr
VLM
269
1,207
0
04 Oct 2018
Robustness May Be at Odds with Accuracy
Robustness May Be at Odds with Accuracy
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Alexander Turner
Aleksander Madry
AAML
108
1,782
0
30 May 2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for
  Regression Learning
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski
Alina Oprea
Battista Biggio
Chang-rui Liu
Cristina Nita-Rotaru
Yue Liu
AAML
85
764
0
01 Apr 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
263
3,485
0
09 Mar 2018
Stochastic Activation Pruning for Robust Adversarial Defense
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet Singh Dhillon
Kamyar Azizzadenesheli
Zachary Chase Lipton
Jeremy Bernstein
Jean Kossaifi
Aran Khanna
Anima Anandkumar
AAML
81
547
0
05 Mar 2018
Essentially No Barriers in Neural Network Energy Landscape
Essentially No Barriers in Neural Network Energy Landscape
Felix Dräxler
K. Veschgini
M. Salmhofer
Fred Hamprecht
MoMe
122
434
0
02 Mar 2018
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Xinyun Chen
Chang-rui Liu
Yue Liu
Kimberly Lu
Basel Alomair
AAMLSILM
143
1,853
0
15 Dec 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model
  Supply Chain
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu
Brendan Dolan-Gavitt
S. Garg
SILM
130
1,782
0
22 Aug 2017
Learning Efficient Convolutional Networks through Network Slimming
Learning Efficient Convolutional Networks through Network Slimming
Zhuang Liu
Jianguo Li
Zhiqiang Shen
Gao Huang
Shoumeng Yan
Changshui Zhang
125
2,426
0
22 Aug 2017
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
163
2,160
0
21 Aug 2017
Channel Pruning for Accelerating Very Deep Neural Networks
Channel Pruning for Accelerating Very Deep Neural Networks
Yihui He
Xiangyu Zhang
Jian Sun
204
2,529
0
19 Jul 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
317
12,131
0
19 Jun 2017
DeepCloak: Masking Deep Neural Network Models for Robustness Against
  Adversarial Samples
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
Ji Gao
Beilun Wang
Zeming Lin
Weilin Xu
Yanjun Qi
AAML
49
90
0
22 Feb 2017
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
472
3,148
0
04 Nov 2016
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OODAAML
282
8,583
0
16 Aug 2016
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
  Atrous Convolution, and Fully Connected CRFs
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen
George Papandreou
Iasonas Kokkinos
Kevin Patrick Murphy
Alan Yuille
SSeg
267
18,267
0
02 Jun 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained
  Quantization and Huffman Coding
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Song Han
Huizi Mao
W. Dally
3DGS
263
8,859
0
01 Oct 2015
Learning both Weights and Connections for Efficient Neural Networks
Learning both Weights and Connections for Efficient Neural Networks
Song Han
Jeff Pool
J. Tran
W. Dally
CVBM
316
6,700
0
08 Jun 2015
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