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Obfuscated Gradients Give a False Sense of Security: Circumventing
  Defenses to Adversarial Examples
v1v2v3v4 (latest)

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1 February 2018
Anish Athalye
Nicholas Carlini
D. Wagner
    AAML
ArXiv (abs)PDFHTML

Papers citing "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples"

32 / 1,982 papers shown
Detecting Adversarial Examples via Neural Fingerprinting
Detecting Adversarial Examples via Neural Fingerprinting
Sumanth Dathathri
Stephan Zheng
Tianwei Yin
Richard M. Murray
Yisong Yue
MLAUAAML
163
0
0
11 Mar 2018
Style Memory: Making a Classifier Network Generative
Style Memory: Making a Classifier Network Generative
R. Wiyatno
Jeff Orchard
111
5
0
05 Mar 2018
Understanding and Enhancing the Transferability of Adversarial Examples
Understanding and Enhancing the Transferability of Adversarial Examples
Lei Wu
Zhanxing Zhu
Cheng Tai
E. Weinan
AAMLSILM
136
116
0
27 Feb 2018
Robust GANs against Dishonest Adversaries
Robust GANs against Dishonest Adversaries
Zhi Xu
Chengtao Li
Stefanie Jegelka
AAML
196
3
0
27 Feb 2018
Hessian-based Analysis of Large Batch Training and Robustness to
  Adversaries
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Z. Yao
A. Gholami
Qi Lei
Kurt Keutzer
Michael W. Mahoney
420
176
0
22 Feb 2018
L2-Nonexpansive Neural Networks
L2-Nonexpansive Neural Networks
Haifeng Qian
M. Wegman
229
76
0
22 Feb 2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using
  JPEG Compression
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Nilaksh Das
Madhuri Shanbhogue
Shang-Tse Chen
Fred Hohman
Siwei Li
Li-Wei Chen
Michael E. Kounavis
Duen Horng Chau
FedMLAAML
206
245
0
19 Feb 2018
Divide, Denoise, and Defend against Adversarial Attacks
Divide, Denoise, and Defend against Adversarial Attacks
Seyed-Mohsen Moosavi-Dezfooli
A. Shrivastava
Oncel Tuzel
AAML
159
46
0
19 Feb 2018
Are Generative Classifiers More Robust to Adversarial Attacks?
Are Generative Classifiers More Robust to Adversarial Attacks?
Yingzhen Li
John Bradshaw
Yash Sharma
AAML
252
85
0
19 Feb 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
J. Uesato
Brendan O'Donoghue
Aaron van den Oord
Pushmeet Kohli
AAML
572
636
0
15 Feb 2018
Fooling OCR Systems with Adversarial Text Images
Fooling OCR Systems with Adversarial Text Images
Congzheng Song
Vitaly Shmatikov
AAML
107
53
0
15 Feb 2018
Predicting Adversarial Examples with High Confidence
Predicting Adversarial Examples with High Confidence
A. Galloway
Graham W. Taylor
M. Moussa
AAML
135
9
0
13 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation
  Invariance for Deep Neural Networks
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku
Issei Sato
Masashi Sugiyama
AAML
481
344
0
12 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy
Certified Robustness to Adversarial Examples with Differential Privacy
Mathias Lécuyer
Vaggelis Atlidakis
Roxana Geambasu
Daniel J. Hsu
Suman Jana
SILMAAML
721
988
0
09 Feb 2018
Blind Pre-Processing: A Robust Defense Method Against Adversarial
  Examples
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Adnan Siraj Rakin
Zhezhi He
Boqing Gong
Deliang Fan
AAML
169
4
0
05 Feb 2018
First-order Adversarial Vulnerability of Neural Networks and Input
  Dimension
First-order Adversarial Vulnerability of Neural Networks and Input Dimension
Carl-Johann Simon-Gabriel
Yann Ollivier
Léon Bottou
Bernhard Schölkopf
David Lopez-Paz
AAML
430
49
0
05 Feb 2018
Secure Detection of Image Manipulation by means of Random Feature
  Selection
Secure Detection of Image Manipulation by means of Random Feature Selection
Zhongfu Chen
B. Tondi
Xiaolong Li
R. Ni
Yao-Min Zhao
Mauro Barni
AAML
197
36
0
02 Feb 2018
Adversarial Spheres
Adversarial Spheres
Justin Gilmer
Luke Metz
Fartash Faghri
S. Schoenholz
M. Raghu
Martin Wattenberg
Ian Goodfellow
AAML
179
6
0
09 Jan 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
Nicholas Carlini
D. Wagner
AAML
215
1,146
0
05 Jan 2018
A General Framework for Adversarial Examples with Objectives
A General Framework for Adversarial Examples with Objectives
Mahmood Sharif
Sruti Bhagavatula
Lujo Bauer
Michael K. Reiter
AAMLGAN
260
216
0
31 Dec 2017
The Robust Manifold Defense: Adversarial Training using Generative
  Models
The Robust Manifold Defense: Adversarial Training using Generative Models
A. Jalal
Andrew Ilyas
C. Daskalakis
A. Dimakis
AAML
418
175
0
26 Dec 2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Battista Biggio
Fabio Roli
AAML
368
1,541
0
08 Dec 2017
Generative Adversarial Perturbations
Generative Adversarial Perturbations
Omid Poursaeed
Isay Katsman
Bicheng Gao
Serge J. Belongie
AAMLGANWIGM
469
385
0
06 Dec 2017
Towards Robust Neural Networks via Random Self-ensemble
Towards Robust Neural Networks via Random Self-ensemble
Xuanqing Liu
Minhao Cheng
Huan Zhang
Cho-Jui Hsieh
FedMLAAML
382
445
0
02 Dec 2017
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
Anurag Arnab
O. Mikšík
Juil Sock
AAML
358
326
0
27 Nov 2017
Reinforcing Adversarial Robustness using Model Confidence Induced by
  Adversarial Training
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu
Uyeong Jang
Jiefeng Chen
Lingjiao Chen
S. Jha
AAML
220
21
0
21 Nov 2017
Evaluating Robustness of Neural Networks with Mixed Integer Programming
Evaluating Robustness of Neural Networks with Mixed Integer Programming
Vincent Tjeng
Kai Y. Xiao
Russ Tedrake
AAML
273
119
0
20 Nov 2017
Adversarial Attacks Beyond the Image Space
Adversarial Attacks Beyond the Image Space
Fangyin Wei
Chenxi Liu
Yu-Siang Wang
Weichao Qiu
Lingxi Xie
Yu-Wing Tai
Chi-Keung Tang
Alan Yuille
AAML
498
160
0
20 Nov 2017
Provable defenses against adversarial examples via the convex outer
  adversarial polytope
Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong
J. Zico Kolter
AAML
671
1,565
0
02 Nov 2017
Provably Minimally-Distorted Adversarial Examples
Provably Minimally-Distorted Adversarial Examples
Nicholas Carlini
Guy Katz
Clark W. Barrett
D. Dill
AAML
210
91
0
29 Sep 2017
Improving Robustness of ML Classifiers against Realizable Evasion
  Attacks Using Conserved Features
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
Liang Tong
Yue Liu
Chen Hajaj
Chaowei Xiao
Ning Zhang
Yevgeniy Vorobeychik
AAMLOOD
265
93
0
28 Aug 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
492
2,944
0
19 May 2017
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