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1706.06083
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Towards Deep Learning Models Resistant to Adversarial Attacks
19 June 2017
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
Re-assign community
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Papers citing
"Towards Deep Learning Models Resistant to Adversarial Attacks"
50 / 7,067 papers shown
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Alex Lamb
Vikas Verma
Kenji Kawaguchi
Alexander Matyasko
Savya Khosla
Arno Solin
Yoshua Bengio
AAML
451
106
0
16 Jun 2019
Defending Against Adversarial Attacks Using Random Forests
Yifan Ding
Liqiang Wang
Huan Zhang
Jinfeng Yi
Deliang Fan
Boqing Gong
AAML
135
15
0
16 Jun 2019
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences
Shashank Kotyan
Danilo Vasconcellos Vargas
Moe Matsuki
256
0
0
15 Jun 2019
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks
Felipe A. Mejia
Paul Gamble
Z. Hampel-Arias
M. Lomnitz
Nina Lopatina
Lucas Tindall
M. Barrios
SILM
145
21
0
15 Jun 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
International Conference on Learning Representations (ICLR), 2019
Huan Zhang
Hongge Chen
Chaowei Xiao
Sven Gowal
Robert Stanforth
Yue Liu
Duane S. Boning
Cho-Jui Hsieh
AAML
360
372
0
14 Jun 2019
Distributionally Robust Counterfactual Risk Minimization
AAAI Conference on Artificial Intelligence (AAAI), 2019
Louis Faury
Ugo Tanielian
Flavian Vasile
E. Smirnova
Elvis Dohmatob
161
46
0
14 Jun 2019
Towards Compact and Robust Deep Neural Networks
Vikash Sehwag
Shiqi Wang
Prateek Mittal
Suman Jana
AAML
139
41
0
14 Jun 2019
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks
Computer Vision and Pattern Recognition (CVPR), 2019
T. Brunner
Frederik Diehl
Alois Knoll
AAML
145
8
0
14 Jun 2019
Adversarial Training Can Hurt Generalization
Aditi Raghunathan
Sang Michael Xie
Fanny Yang
John C. Duchi
Abigail Z. Jacobs
323
251
0
14 Jun 2019
Adversarial Robustness Assessment: Why both
L
0
L_0
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0
and
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∞
L_\infty
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∞
Attacks Are Necessary
Shashank Kotyan
Danilo Vasconcellos Vargas
AAML
168
8
0
14 Jun 2019
Lower Bounds for Adversarially Robust PAC Learning
International Conference on Machine Learning and Applications (ICMLA), 2019
Dimitrios I. Diochnos
Saeed Mahloujifar
Mohammad Mahmoody
AAML
239
27
0
13 Jun 2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
R. Sahay
Rehana Mahfuz
Aly El Gamal
AAML
75
5
0
13 Jun 2019
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Neural Information Processing Systems (NeurIPS), 2019
Guang-He Lee
Yang Yuan
Shiyu Chang
Tommi Jaakkola
AAML
248
134
0
12 Jun 2019
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Neural Information Processing Systems (NeurIPS), 2019
Mahyar Fazlyab
Avi Schwarzschild
Hamed Hassani
M. Morari
George J. Pappas
409
522
0
12 Jun 2019
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Neural Information Processing Systems (NeurIPS), 2019
Ziang Yan
Yiwen Guo
Changshui Zhang
AAML
167
118
0
11 Jun 2019
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Kaidi Xu
Hongge Chen
Sijia Liu
Pin-Yu Chen
Tsui-Wei Weng
Mingyi Hong
Xue Lin
AAML
399
506
0
10 Jun 2019
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective
Lu Wang
Xuanqing Liu
Jinfeng Yi
Zhi Zhou
Cho-Jui Hsieh
AAML
155
22
0
10 Jun 2019
Robustness Verification of Tree-based Models
Neural Information Processing Systems (NeurIPS), 2019
Hongge Chen
Huan Zhang
Si Si
Yang Li
Duane S. Boning
Cho-Jui Hsieh
AAML
223
87
0
10 Jun 2019
Intriguing properties of adversarial training at scale
International Conference on Learning Representations (ICLR), 2019
Cihang Xie
Alan Yuille
AAML
191
67
0
10 Jun 2019
Improved Adversarial Robustness via Logit Regularization Methods
Cecilia Summers
M. Dinneen
AAML
96
7
0
10 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Neural Information Processing Systems (NeurIPS), 2019
Hadi Salman
Greg Yang
Jungshian Li
Pengchuan Zhang
Huan Zhang
Ilya P. Razenshteyn
Sébastien Bubeck
AAML
678
591
0
09 Jun 2019
Adversarial Attack Generation Empowered by Min-Max Optimization
Neural Information Processing Systems (NeurIPS), 2019
Jingkang Wang
Tianyun Zhang
Sijia Liu
Pin-Yu Chen
Jiacen Xu
M. Fardad
Yangqiu Song
AAML
367
43
0
09 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Neural Information Processing Systems (NeurIPS), 2019
Maksym Andriushchenko
Matthias Hein
214
66
0
08 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution
AAAI Conference on Artificial Intelligence (AAAI), 2019
Puyudi Yang
Jianbo Chen
Cho-Jui Hsieh
Jane-ling Wang
Sai Li
AAML
173
112
0
08 Jun 2019
Robustness for Non-Parametric Classification: A Generic Attack and Defense
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Yao-Yuan Yang
Cyrus Rashtchian
Yizhen Wang
Kamalika Chaudhuri
SILM
AAML
252
44
0
07 Jun 2019
A cryptographic approach to black box adversarial machine learning
Kevin Shi
Daniel J. Hsu
Allison Bishop
AAML
79
3
0
07 Jun 2019
Kernelized Capsule Networks
Taylor W. Killian
Justin A. Goodwin
Olivia M. Brown
Sung-Hyun Son
GAN
129
3
0
07 Jun 2019
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Yan Li
Ethan X. Fang
Huan Xu
T. Zhao
267
18
0
07 Jun 2019
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
Nature Machine Intelligence (NMI), 2019
Walt Woods
Jack H Chen
C. Teuscher
AAML
236
49
0
07 Jun 2019
Robust Attacks against Multiple Classifiers
Juan C. Perdomo
Yaron Singer
AAML
144
11
0
06 Jun 2019
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Raphael Gontijo-Lopes
Dong Yin
Ben Poole
Justin Gilmer
E. D. Cubuk
AAML
308
220
0
06 Jun 2019
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
Yujun Shi
B. Liao
Guangyong Chen
Yun-Hai Liu
Ming-Ming Cheng
Jiashi Feng
AAML
113
2
0
06 Jun 2019
Image Synthesis with a Single (Robust) Classifier
Neural Information Processing Systems (NeurIPS), 2019
Shibani Santurkar
Dimitris Tsipras
Brandon Tran
Andrew Ilyas
Logan Engstrom
Aleksander Madry
AAML
152
36
0
06 Jun 2019
Should Adversarial Attacks Use Pixel p-Norm?
Ayon Sen
Xiaojin Zhu
Liam Marshall
Robert D. Nowak
131
21
0
06 Jun 2019
Query-efficient Meta Attack to Deep Neural Networks
International Conference on Learning Representations (ICLR), 2019
Jiawei Du
Hu Zhang
Qiufeng Wang
Yi Yang
Jiashi Feng
AAML
201
86
0
06 Jun 2019
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Xuanqing Liu
Tesi Xiao
Si Si
Qin Cao
Sanjiv Kumar
Cho-Jui Hsieh
209
158
0
05 Jun 2019
MNIST-C: A Robustness Benchmark for Computer Vision
Norman Mu
Justin Gilmer
212
236
0
05 Jun 2019
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization
IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2019
Tyler Sypherd
Mario Díaz
J. Cava
Gautam Dasarathy
Peter Kairouz
Lalitha Sankar
556
35
0
05 Jun 2019
Enhancing Gradient-based Attacks with Symbolic Intervals
Shiqi Wang
Yizheng Chen
Ahmed Abdou
Suman Jana
AAML
108
15
0
05 Jun 2019
Do Image Classifiers Generalize Across Time?
IEEE International Conference on Computer Vision (ICCV), 2019
Vaishaal Shankar
Achal Dave
Rebecca Roelofs
Deva Ramanan
Benjamin Recht
Ludwig Schmidt
418
85
0
05 Jun 2019
Multi-way Encoding for Robustness
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2019
Donghyun Kim
Sarah Adel Bargal
Jianming Zhang
Stan Sclaroff
AAML
106
2
0
05 Jun 2019
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth
Yannic Kilcher
Thomas Hofmann
200
13
0
04 Jun 2019
What do AI algorithms actually learn? - On false structures in deep learning
L. Thesing
Vegard Antun
A. Hansen
103
21
0
04 Jun 2019
Understanding the Limitations of Conditional Generative Models
International Conference on Learning Representations (ICLR), 2019
Ethan Fetaya
J. Jacobsen
Will Grathwohl
R. Zemel
283
62
0
04 Jun 2019
Architecture Selection via the Trade-off Between Accuracy and Robustness
Zhun Deng
Cynthia Dwork
Jialiang Wang
Yao-Min Zhao
AAML
236
5
0
04 Jun 2019
Correctness Verification of Neural Networks
Yichen Yang
Martin Rinard
AAML
165
13
0
03 Jun 2019
Adversarial Robustness as a Prior for Learned Representations
Logan Engstrom
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Brandon Tran
Aleksander Madry
OOD
AAML
230
63
0
03 Jun 2019
DAWN: Dynamic Adversarial Watermarking of Neural Networks
ACM Multimedia (ACM MM), 2019
S. Szyller
B. Atli
Samuel Marchal
Nadarajah Asokan
MLAU
AAML
310
210
0
03 Jun 2019
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression
E. Balda
Arash Behboodi
Niklas Koep
R. Mathar
AAML
168
9
0
03 Jun 2019
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
The European Symposium on Artificial Neural Networks (ESANN), 2019
P. Morawiecki
Przemysław Spurek
Marek Śmieja
Jacek Tabor
AAML
OOD
109
9
0
03 Jun 2019
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