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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
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Papers citing
"Towards Deep Learning Models Resistant to Adversarial Attacks"
50 / 6,612 papers shown
Title
Rearchitecting Classification Frameworks For Increased Robustness
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Brian Tang
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S. Jha
Xi Wu
AAML
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100
8
0
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Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling
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Andre Cianflone
William L. Hamilton
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75
7
0
26 May 2019
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb
Jonathan Binas
Anirudh Goyal
Sandeep Subramanian
Ioannis Mitliagkas
Denis Kazakov
Yoshua Bengio
Michael C. Mozer
OOD
45
3
0
26 May 2019
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders
Hebi Li
Qi Xiao
Shixin Tian
Jin Tian
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68
4
0
26 May 2019
Adversarial Distillation for Ordered Top-k Attacks
Zekun Zhang
Tianfu Wu
AAML
44
2
0
25 May 2019
Enhancing Adversarial Defense by k-Winners-Take-All
Chang Xiao
Peilin Zhong
Changxi Zheng
AAML
80
99
0
25 May 2019
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks
Yuanshun Yao
Huiying Li
Haitao Zheng
Ben Y. Zhao
AAML
55
13
0
24 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Liwei Song
Reza Shokri
Prateek Mittal
SILM
MIACV
AAML
94
248
0
24 May 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi
S. Maeda
Masanori Koyama
Takeru Miyato
OOD
92
131
0
24 May 2019
Robust Attribution Regularization
Jiefeng Chen
Xi Wu
Vaibhav Rastogi
Yingyu Liang
S. Jha
OOD
59
83
0
23 May 2019
Thwarting finite difference adversarial attacks with output randomization
Haidar Khan
Daniel Park
Azer Khan
B. Yener
SILM
AAML
52
0
0
23 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks
Tianyuan Zhang
Zhanxing Zhu
AAML
GAN
FAtt
125
161
0
23 May 2019
Adversarially Robust Distillation
Micah Goldblum
Liam H. Fowl
Soheil Feizi
Tom Goldstein
AAML
94
213
0
23 May 2019
A Direct Approach to Robust Deep Learning Using Adversarial Networks
Huaxia Wang
Chun-Nam Yu
GAN
AAML
OOD
76
77
0
23 May 2019
Convergence and Margin of Adversarial Training on Separable Data
Zachary B. Charles
Shashank Rajput
S. Wright
Dimitris Papailiopoulos
AAML
71
17
0
22 May 2019
Adversarially robust transfer learning
Ali Shafahi
Parsa Saadatpanah
Chen Zhu
Amin Ghiasi
Christoph Studer
David Jacobs
Tom Goldstein
OOD
52
117
0
20 May 2019
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Shubham Sharma
Jette Henderson
Joydeep Ghosh
85
88
0
20 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain
Lei Bu
Yuchao Duan
Fu Song
Zhe Zhao
AAML
114
18
0
19 May 2019
What Do Adversarially Robust Models Look At?
Takahiro Itazuri
Yoshihiro Fukuhara
Hirokatsu Kataoka
Shigeo Morishima
32
5
0
19 May 2019
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
Z. Din
P. Tigas
Samuel T. King
B. Livshits
VLM
160
29
0
17 May 2019
Simple Black-box Adversarial Attacks
Chuan Guo
Jacob R. Gardner
Yurong You
A. Wilson
Kilian Q. Weinberger
AAML
78
582
0
17 May 2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models
Nicholas Carlini
ELM
68
14
0
17 May 2019
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon
Gaon An
Hyun Oh Song
AAML
MLAU
88
136
0
16 May 2019
On Norm-Agnostic Robustness of Adversarial Training
Bai Li
Changyou Chen
Wenlin Wang
Lawrence Carin
OOD
SILM
68
7
0
15 May 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Chen Zhu
Wenjie Huang
Ali Shafahi
Hengduo Li
Gavin Taylor
Christoph Studer
Tom Goldstein
125
286
0
15 May 2019
Adversarial Examples for Electrocardiograms
Xintian Han
Yuxuan Hu
L. Foschini
L. Chinitz
Lior Jankelson
Rajesh Ranganath
AAML
MedIm
49
4
0
13 May 2019
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
M. Singh
Abhishek Sinha
Nupur Kumari
Harshitha Machiraju
Balaji Krishnamurthy
V. Balasubramanian
AAML
56
61
0
13 May 2019
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks
Olakunle Ibitoye
Omair Shafiq
Ashraf Matrawy
61
164
0
13 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun
Dongyoon Han
Seong Joon Oh
Sanghyuk Chun
Junsuk Choe
Y. Yoo
OOD
660
4,833
0
13 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples
Qun Song
Zhenyu Yan
Rui Tan
AAML
47
21
0
11 May 2019
Interpreting and Evaluating Neural Network Robustness
Fuxun Yu
Zhuwei Qin
Chenchen Liu
Liang Zhao
Yanzhi Wang
Xiang Chen
AAML
57
56
0
10 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann
Sebastian Lunz
Peter Maass
Carola-Bibiane Schönlieb
AAML
FAtt
63
162
0
10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness
Evelyn Duesterwald
Anupama Murthi
Ganesh Venkataraman
M. Sinn
Deepak Vijaykeerthy
AAML
52
7
0
09 May 2019
Learning Interpretable Features via Adversarially Robust Optimization
Ashkan Khakzar
Shadi Albarqouni
Nassir Navab
MedIm
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55
14
0
09 May 2019
Adversarial Defense Framework for Graph Neural Network
Shen Wang
Zhengzhang Chen
Jingchao Ni
Xiao Yu
Zhichun Li
Haifeng Chen
Philip S. Yu
AAML
GNN
71
28
0
09 May 2019
ROSA: Robust Salient Object Detection against Adversarial Attacks
Haofeng Li
Guanbin Li
Yizhou Yu
AAML
70
29
0
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Does Data Augmentation Lead to Positive Margin?
Shashank Rajput
Zhili Feng
Zachary B. Charles
Po-Ling Loh
Dimitris Papailiopoulos
86
38
0
08 May 2019
An Empirical Evaluation of Adversarial Robustness under Transfer Learning
Todor Davchev
Timos Korres
Stathi Fotiadis
N. Antonopoulos
S. Ramamoorthy
AAML
38
0
0
07 May 2019
Adaptive Generation of Unrestricted Adversarial Inputs
Isaac Dunn
Hadrien Pouget
T. Melham
Daniel Kroening
AAML
61
7
0
07 May 2019
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas
Shibani Santurkar
Dimitris Tsipras
Logan Engstrom
Brandon Tran
Aleksander Madry
SILM
119
1,846
0
06 May 2019
Batch Normalization is a Cause of Adversarial Vulnerability
A. Galloway
A. Golubeva
T. Tanay
M. Moussa
Graham W. Taylor
ODL
AAML
84
80
0
06 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
Vikash Sehwag
A. Bhagoji
Liwei Song
Chawin Sitawarin
Daniel Cullina
M. Chiang
Prateek Mittal
OODD
79
26
0
05 May 2019
CharBot: A Simple and Effective Method for Evading DGA Classifiers
Jonathan Peck
Claire Nie
R. Sivaguru
Charles Grumer
Femi G. Olumofin
Bin Yu
A. Nascimento
Martine De Cock
AAML
48
44
0
03 May 2019
Transfer of Adversarial Robustness Between Perturbation Types
Daniel Kang
Yi Sun
Tom B. Brown
Dan Hendrycks
Jacob Steinhardt
AAML
71
49
0
03 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Dinghuai Zhang
Tianyuan Zhang
Yiping Lu
Zhanxing Zhu
Bin Dong
AAML
130
362
0
02 May 2019
Adversarial Training with Voronoi Constraints
Marc Khoury
Dylan Hadfield-Menell
AAML
63
24
0
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NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li
Lijun Li
Liqiang Wang
Tong Zhang
Boqing Gong
AAML
86
245
0
01 May 2019
Dropping Pixels for Adversarial Robustness
Hossein Hosseini
Sreeram Kannan
Radha Poovendran
44
16
0
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A scalable saliency-based Feature selection method with instance level information
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V. Bolón-Canedo
Amparo Alonso-Betanzos
João Gama
FAtt
62
13
0
30 Apr 2019
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
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D. Bacciu
Battista Biggio
AAML
83
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0
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