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Towards Deep Neural Network Architectures Robust to Adversarial Examples
v1v2v3v4 (latest)

Towards Deep Neural Network Architectures Robust to Adversarial Examples

International Conference on Learning Representations (ICLR), 2014
11 December 2014
S. Gu
Luca Rigazio
    AAML
ArXiv (abs)PDFHTML

Papers citing "Towards Deep Neural Network Architectures Robust to Adversarial Examples"

50 / 417 papers shown
Killing four birds with one Gaussian process: the relation between
  different test-time attacks
Killing four birds with one Gaussian process: the relation between different test-time attacks
Kathrin Grosse
M. Smith
Michael Backes
AAML
181
2
0
06 Jun 2018
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Jan Svoboda
Jonathan Masci
Federico Monti
M. Bronstein
Leonidas Guibas
AAMLGNN
179
42
0
31 May 2018
Resisting Adversarial Attacks using Gaussian Mixture Variational
  Autoencoders
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Partha Ghosh
Arpan Losalka
Michael J. Black
AAML
240
81
0
31 May 2018
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Vignesh Srinivasan
Arturo Marbán
K. Müller
Wojciech Samek
Shinichi Nakajima
AAML
157
9
0
30 May 2018
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
M. Alzantot
Yash Sharma
Supriyo Chakraborty
Huan Zhang
Cho-Jui Hsieh
Mani B. Srivastava
AAML
322
279
0
28 May 2018
Defending Against Adversarial Attacks by Leveraging an Entire GAN
Defending Against Adversarial Attacks by Leveraging an Entire GAN
G. Santhanam
Paulina Grnarova
AAML
116
40
0
27 May 2018
Laplacian Networks: Bounding Indicator Function Smoothness for Neural
  Network Robustness
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness
Carlos Lassance
Vincent Gripon
Antonio Ortega
AAML
198
18
0
24 May 2018
Towards the first adversarially robust neural network model on MNIST
Towards the first adversarially robust neural network model on MNIST
Lukas Schott
Jonas Rauber
Matthias Bethge
Wieland Brendel
AAMLOOD
353
380
0
23 May 2018
Bidirectional Learning for Robust Neural Networks
Bidirectional Learning for Robust Neural Networks
S. Pontes-Filho
Marcus Liwicki
137
9
0
21 May 2018
Constructing Unrestricted Adversarial Examples with Generative Models
Constructing Unrestricted Adversarial Examples with Generative Models
Yang Song
Rui Shu
Nate Kushman
Stefano Ermon
GANAAML
586
341
0
21 May 2018
Detecting Adversarial Samples for Deep Neural Networks through Mutation
  Testing
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Jingyi Wang
Jun Sun
Peixin Zhang
Xinyu Wang
AAML
196
41
0
14 May 2018
Fortified Networks: Improving the Robustness of Deep Networks by
  Modeling the Manifold of Hidden Representations
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
Alex Lamb
Jonathan Binas
Anirudh Goyal
Dmitriy Serdyuk
Sandeep Subramanian
Alexia Jolicoeur-Martineau
Yoshua Bengio
OOD
213
45
0
07 Apr 2018
Defending against Adversarial Images using Basis Functions
  Transformations
Defending against Adversarial Images using Basis Functions Transformations
Uri Shaham
J. Garritano
Yutaro Yamada
Ethan Weinberger
A. Cloninger
Xiuyuan Cheng
Kelly P. Stanton
Y. Kluger
AAML
159
61
0
28 Mar 2018
The Effects of JPEG and JPEG2000 Compression on Attacks using
  Adversarial Examples
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
Ayse Elvan Aydemir
A. Temi̇zel
T. Taşkaya-Temizel
AAML
129
37
0
28 Mar 2018
Clipping free attacks against artificial neural networks
Clipping free attacks against artificial neural networks
B. Addad
Jérôme Kodjabachian
Christophe Meyer
AAML
66
1
0
26 Mar 2018
Detecting Adversarial Perturbations with Saliency
Detecting Adversarial Perturbations with Saliency
Chiliang Zhang
Zhimou Yang
Zuochang Ye
AAML
90
33
0
23 Mar 2018
Defending against Adversarial Attack towards Deep Neural Networks via
  Collaborative Multi-task Training
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task TrainingIEEE Transactions on Dependable and Secure Computing (IEEE TDSC), 2018
Derui Wang
Chaoran Li
S. Wen
Surya Nepal
Yang Xiang
AAML
252
35
0
14 Mar 2018
Retrieval-Augmented Convolutional Neural Networks for Improved
  Robustness against Adversarial Examples
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
Jake Zhao
Dong Wang
AAML
242
20
0
26 Feb 2018
Sensitivity and Generalization in Neural Networks: an Empirical Study
Sensitivity and Generalization in Neural Networks: an Empirical Study
Roman Novak
Yasaman Bahri
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
AAML
395
479
0
23 Feb 2018
Deep Defense: Training DNNs with Improved Adversarial Robustness
Deep Defense: Training DNNs with Improved Adversarial Robustness
Ziang Yan
Yiwen Guo
Changshui Zhang
AAML
337
121
0
23 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
163
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
255
85
0
19 Feb 2018
Security and Privacy Approaches in Mixed Reality: A Literature Survey
Security and Privacy Approaches in Mixed Reality: A Literature Survey
Jaybie A. de Guzman
Kanchana Thilakarathna
Aruna Seneviratne
229
152
0
15 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
578
636
0
15 Feb 2018
Security Analysis and Enhancement of Model Compressed Deep Learning
  Systems under Adversarial Attacks
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
Qi Liu
Tao Liu
Zihao Liu
Yanzhi Wang
Yier Jin
Wujie Wen
AAML
228
49
0
14 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
493
345
0
12 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
Hardening Deep Neural Networks via Adversarial Model Cascades
Hardening Deep Neural Networks via Adversarial Model Cascades
Deepak Vijaykeerthy
Anshuman Suri
S. Mehta
Ponnurangam Kumaraguru
AAML
204
9
0
02 Feb 2018
Visual Analytics in Deep Learning: An Interrogative Survey for the Next
  Frontiers
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Fred Hohman
Minsuk Kahng
Robert S. Pienta
Duen Horng Chau
OODHAI
255
584
0
21 Jan 2018
Characterizing Adversarial Subspaces Using Local Intrinsic
  Dimensionality
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Jiabo He
Yue Liu
Yisen Wang
S. Erfani
S. Wijewickrema
Grant Schoenebeck
Basel Alomair
Michael E. Houle
James Bailey
AAML
414
798
0
08 Jan 2018
Spatially Transformed Adversarial Examples
Spatially Transformed Adversarial Examples
Chaowei Xiao
Jun-Yan Zhu
Yue Liu
Warren He
M. Liu
Basel Alomair
AAML
400
556
0
08 Jan 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
Lin Wang
AAML
495
1,993
0
02 Jan 2018
Gradient Regularization Improves Accuracy of Discriminative Models
Gradient Regularization Improves Accuracy of Discriminative Models
D. Varga
Adrián Csiszárik
Zsolt Zombori
198
54
0
28 Dec 2017
Exploring the Space of Black-box Attacks on Deep Neural Networks
Exploring the Space of Black-box Attacks on Deep Neural Networks
A. Bhagoji
Warren He
Yue Liu
Basel Alomair
AAML
67
75
0
27 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
420
175
0
26 Dec 2017
Adversarial Examples: Attacks and Defenses for Deep Learning
Adversarial Examples: Attacks and Defenses for Deep LearningIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2017
Xiaoyong Yuan
Pan He
Qile Zhu
Xiaolin Li
SILMAAML
592
1,746
0
19 Dec 2017
Defense against Adversarial Attacks Using High-Level Representation
  Guided Denoiser
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Fangzhou Liao
Ming Liang
Yinpeng Dong
Tianyu Pang
Xiaolin Hu
Jun Zhu
451
986
0
08 Dec 2017
Adversarial Examples that Fool Detectors
Adversarial Examples that Fool Detectors
Jiajun Lu
Hussein Sibai
Evan Fabry
AAML
141
156
0
07 Dec 2017
Towards Practical Verification of Machine Learning: The Case of Computer
  Vision Systems
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems
Kexin Pei
Linjie Zhu
Yinzhi Cao
Junfeng Yang
Carl Vondrick
Suman Jana
AAML
318
108
0
05 Dec 2017
Improving Network Robustness against Adversarial Attacks with Compact
  Convolution
Improving Network Robustness against Adversarial Attacks with Compact Convolution
Rajeev Ranjan
S. Sankaranarayanan
Carlos D. Castillo
Rama Chellappa
AAML
124
14
0
03 Dec 2017
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Jason Jo
Yoshua Bengio
AAML
162
260
0
30 Nov 2017
Adversary Detection in Neural Networks via Persistent Homology
Adversary Detection in Neural Networks via Persistent Homology
Thomas Gebhart
Paul Schrater
AAML
98
26
0
28 Nov 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
360
326
0
27 Nov 2017
Butterfly Effect: Bidirectional Control of Classification Performance by
  Small Additive Perturbation
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
Y. Yoo
Seonguk Park
Junyoung Choi
Sangdoo Yun
Nojun Kwak
AAML
180
4
0
27 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
Enhanced Attacks on Defensively Distilled Deep Neural Networks
Enhanced Attacks on Defensively Distilled Deep Neural Networks
Yujia Liu
Weiming Zhang
Shaohua Li
Nenghai Yu
AAML
135
6
0
16 Nov 2017
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples
Jihun Hamm
Akshay Mehra
AAML
238
7
0
12 Nov 2017
A Unified View of Piecewise Linear Neural Network Verification
A Unified View of Piecewise Linear Neural Network Verification
Rudy Bunel
Ilker Turkaslan
Juil Sock
Pushmeet Kohli
M. P. Kumar
AAML
296
74
0
01 Nov 2017
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