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Explaining and Harnessing Adversarial Examples

Explaining and Harnessing Adversarial Examples

20 December 2014
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
    AAML
    GAN
ArXivPDFHTML

Papers citing "Explaining and Harnessing Adversarial Examples"

50 / 3,621 papers shown
Title
On the (Statistical) Detection of Adversarial Examples
On the (Statistical) Detection of Adversarial Examples
Kathrin Grosse
Praveen Manoharan
Nicolas Papernot
Michael Backes
Patrick McDaniel
AAML
39
709
0
21 Feb 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
249
1,842
0
03 Feb 2017
Deep Reinforcement Learning: An Overview
Deep Reinforcement Learning: An Overview
Yuxi Li
OffRL
VLM
104
1,503
0
25 Jan 2017
Towards Principled Methods for Training Generative Adversarial Networks
Towards Principled Methods for Training Generative Adversarial Networks
Martín Arjovsky
M. Nault
GAN
48
2,097
0
17 Jan 2017
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Rock Stevens
H. Aggarwal
Himani Arora
Sanghyun Hong
M. Hicks
Chetan Arora
SILM
AAML
13
18
0
17 Jan 2017
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Vahid Behzadan
Arslan Munir
AAML
SILM
21
274
0
16 Jan 2017
Comprehension-guided referring expressions
Comprehension-guided referring expressions
Ruotian Luo
Gregory Shakhnarovich
ObjD
29
171
0
12 Jan 2017
Dense Associative Memory is Robust to Adversarial Inputs
Dense Associative Memory is Robust to Adversarial Inputs
Dmitry Krotov
J. Hopfield
AAML
31
111
0
04 Jan 2017
Simple Black-Box Adversarial Perturbations for Deep Networks
Simple Black-Box Adversarial Perturbations for Deep Networks
Nina Narodytska
S. Kasiviswanathan
AAML
27
237
0
19 Dec 2016
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,683
0
05 Dec 2016
Learning Adversary-Resistant Deep Neural Networks
Learning Adversary-Resistant Deep Neural Networks
Qinglong Wang
Wenbo Guo
Kaixuan Zhang
Alexander Ororbia
Masashi Sugiyama
Xue Liu
C. Lee Giles
AAML
23
43
0
05 Dec 2016
Local minima in training of neural networks
Local minima in training of neural networks
G. Swirszcz
Wojciech M. Czarnecki
Razvan Pascanu
ODL
37
73
0
19 Nov 2016
LOTS about Attacking Deep Features
LOTS about Attacking Deep Features
Andras Rozsa
Manuel Günther
Terrance E. Boult
AAML
46
42
0
18 Nov 2016
VisualBackProp: efficient visualization of CNNs
VisualBackProp: efficient visualization of CNNs
Mariusz Bojarski
A. Choromańska
K. Choromanski
Bernhard Firner
L. Jackel
Urs Muller
Karol Zieba
FAtt
36
74
0
16 Nov 2016
Delving into Transferable Adversarial Examples and Black-box Attacks
Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu
Xinyun Chen
Chang-rui Liu
D. Song
AAML
28
1,722
0
08 Nov 2016
Adversarial Machine Learning at Scale
Adversarial Machine Learning at Scale
Alexey Kurakin
Ian Goodfellow
Samy Bengio
AAML
298
3,113
0
04 Nov 2016
Towards Lifelong Self-Supervision: A Deep Learning Direction for
  Robotics
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
J. M. Wong
27
11
0
01 Nov 2016
Universal adversarial perturbations
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
AAML
50
2,514
0
26 Oct 2016
Safety Verification of Deep Neural Networks
Safety Verification of Deep Neural Networks
Xiaowei Huang
Marta Kwiatkowska
Sen Wang
Min Wu
AAML
183
933
0
21 Oct 2016
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of
  Convolutional Neural Networks Approaches
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
E. Rodner
Marcel Simon
Robert B. Fisher
Joachim Denzler
22
39
0
21 Oct 2016
Digital Makeup from Internet Images
Digital Makeup from Internet Images
Asad Khan
Muhammad Ahmad
Yudong Guo
Ligang Liu
DiffM
25
2
0
16 Oct 2016
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based
  Localization
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Ramprasaath R. Selvaraju
Michael Cogswell
Abhishek Das
Ramakrishna Vedantam
Devi Parikh
Dhruv Batra
FAtt
71
19,607
0
07 Oct 2016
DeepDGA: Adversarially-Tuned Domain Generation and Detection
DeepDGA: Adversarially-Tuned Domain Generation and Detection
Hyrum S. Anderson
Jonathan Woodbridge
Bobby Filar
AAML
16
197
0
06 Oct 2016
Supervision via Competition: Robot Adversaries for Learning Tasks
Supervision via Competition: Robot Adversaries for Learning Tasks
Lerrel Pinto
James Davidson
Abhinav Gupta
SSL
24
82
0
05 Oct 2016
Adversary Resistant Deep Neural Networks with an Application to Malware
  Detection
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
Qinglong Wang
Wenbo Guo
Kaixuan Zhang
Alexander Ororbia
Masashi Sugiyama
C. Lee Giles
Xue Liu
AAML
27
173
0
05 Oct 2016
Fitted Learning: Models with Awareness of their Limits
Fitted Learning: Models with Awareness of their Limits
Navid Kardan
Kenneth O. Stanley
CLL
32
16
0
07 Sep 2016
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Arild Nøkland
ODL
31
449
0
06 Sep 2016
Robustness of classifiers: from adversarial to random noise
Robustness of classifiers: from adversarial to random noise
Alhussein Fawzi
Seyed-Mohsen Moosavi-Dezfooli
P. Frossard
AAML
16
367
0
31 Aug 2016
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
77
8,460
0
16 Aug 2016
A study of the effect of JPG compression on adversarial images
A study of the effect of JPG compression on adversarial images
Gintare Karolina Dziugaite
Zoubin Ghahramani
Daniel M. Roy
AAML
38
531
0
02 Aug 2016
Early Methods for Detecting Adversarial Images
Early Methods for Detecting Adversarial Images
Dan Hendrycks
Kevin Gimpel
AAML
26
235
0
01 Aug 2016
Unsupervised Learning from Continuous Video in a Scalable Predictive
  Recurrent Network
Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network
Filip Piekniewski
Patryk A. Laurent
Csaba Petre
Micah Richert
Dimitry Fisher
Todd Hylton
20
17
0
22 Jul 2016
On the Effectiveness of Defensive Distillation
On the Effectiveness of Defensive Distillation
Nicolas Papernot
Patrick McDaniel
AAML
17
64
0
18 Jul 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
326
5,847
0
08 Jul 2016
Towards Verified Artificial Intelligence
Towards Verified Artificial Intelligence
S. Seshia
Dorsa Sadigh
S. Shankar Sastry
20
203
0
27 Jun 2016
Concrete Problems in AI Safety
Concrete Problems in AI Safety
Dario Amodei
C. Olah
Jacob Steinhardt
Paul Christiano
John Schulman
Dandelion Mané
77
2,324
0
21 Jun 2016
On the Expressive Power of Deep Neural Networks
On the Expressive Power of Deep Neural Networks
M. Raghu
Ben Poole
Jon M. Kleinberg
Surya Ganguli
Jascha Narain Sohl-Dickstein
29
778
0
16 Jun 2016
Dense Associative Memory for Pattern Recognition
Dense Associative Memory for Pattern Recognition
Dmitry Krotov
J. Hopfield
10
331
0
03 Jun 2016
Deep convolutional neural networks for predominant instrument
  recognition in polyphonic music
Deep convolutional neural networks for predominant instrument recognition in polyphonic music
Yoonchang Han
Jae‐Hun Kim
Kyogu Lee
33
203
0
31 May 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks
  using Adversarial Samples
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
SILM
AAML
17
1,729
0
24 May 2016
Measuring Neural Net Robustness with Constraints
Measuring Neural Net Robustness with Constraints
Osbert Bastani
Yani Andrew Ioannou
Leonidas Lampropoulos
Dimitrios Vytiniotis
A. Nori
A. Criminisi
AAML
17
422
0
24 May 2016
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Nicolas Papernot
Patrick McDaniel
A. Swami
Richard E. Harang
AAML
GAN
SILM
8
451
0
28 Apr 2016
Makeup like a superstar: Deep Localized Makeup Transfer Network
Makeup like a superstar: Deep Localized Makeup Transfer Network
Si Liu
Xinyu Ou
Ruihe Qian
Wei Wang
Xiaochun Cao
OOD
31
2
0
25 Apr 2016
Humans and deep networks largely agree on which kinds of variation make
  object recognition harder
Humans and deep networks largely agree on which kinds of variation make object recognition harder
Saeed Reza Kheradpisheh
M. Ghodrati
M. Ganjtabesh
T. Masquelier
OOD
35
34
0
21 Apr 2016
Understanding How Image Quality Affects Deep Neural Networks
Understanding How Image Quality Affects Deep Neural Networks
Samuel F. Dodge
Lina Karam
VLM
26
722
0
14 Apr 2016
A General Retraining Framework for Scalable Adversarial Classification
A General Retraining Framework for Scalable Adversarial Classification
Bo Li
Yevgeniy Vorobeychik
Xinyun Chen
AAML
19
32
0
09 Apr 2016
Evolution of active categorical image classification via saccadic eye
  movement
Evolution of active categorical image classification via saccadic eye movement
Randal S. Olson
J. Moore
C. Adami
21
4
0
27 Mar 2016
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic
  Extraction of Semantics, Formation of Integrated Concepts and Re-selection
  Features for Ambiguity
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
Peijie Yin
Hong Qiao
Wei Wu
Lu Qi
Yinlin Li
Shanlin Zhong
Bo Zhang
11
8
0
25 Mar 2016
Deep Learning in Bioinformatics
Deep Learning in Bioinformatics
Seonwoo Min
Byunghan Lee
Sungroh Yoon
AI4CE
3DV
36
1,351
0
21 Mar 2016
Multifaceted Feature Visualization: Uncovering the Different Types of
  Features Learned By Each Neuron in Deep Neural Networks
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
Anh Totti Nguyen
J. Yosinski
Jeff Clune
28
324
0
11 Feb 2016
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