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Biologically inspired protection of deep networks from adversarial
  attacks

Biologically inspired protection of deep networks from adversarial attacks

27 March 2017
Aran Nayebi
Surya Ganguli
    AAML
ArXiv (abs)PDFHTML

Papers citing "Biologically inspired protection of deep networks from adversarial attacks"

12 / 62 papers shown
Stochastic Activation Pruning for Robust Adversarial Defense
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet Singh Dhillon
Kamyar Azizzadenesheli
Zachary Chase Lipton
Jeremy Bernstein
Jean Kossaifi
Aran Khanna
Anima Anandkumar
AAML
376
572
0
05 Mar 2018
Certified Defenses against Adversarial Examples
Certified Defenses against Adversarial Examples
Aditi Raghunathan
Jacob Steinhardt
Abigail Z. Jacobs
AAML
406
992
0
29 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
507
1,997
0
02 Jan 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box
  Machine Learning Models
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Wieland Brendel
Jonas Rauber
Matthias Bethge
AAML
314
1,474
0
12 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
475
989
0
08 Dec 2017
Improving the Adversarial Robustness and Interpretability of Deep Neural
  Networks by Regularizing their Input Gradients
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
A. Ross
Finale Doshi-Velez
AAML
391
724
0
26 Nov 2017
Intriguing Properties of Adversarial Examples
Intriguing Properties of Adversarial Examples
E. D. Cubuk
Barret Zoph
S. Schoenholz
Quoc V. Le
AAML
114
88
0
08 Nov 2017
PixelDefend: Leveraging Generative Models to Understand and Defend
  against Adversarial Examples
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial ExamplesInternational Conference on Learning Representations (ICLR), 2017
Yang Song
Taesup Kim
Sebastian Nowozin
Stefano Ermon
Nate Kushman
AAML
420
824
0
30 Oct 2017
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Analyzing the Robustness of Nearest Neighbors to Adversarial ExamplesInternational Conference on Machine Learning (ICML), 2017
Yizhen Wang
S. Jha
Kamalika Chaudhuri
AAML
499
159
0
13 Jun 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
510
2,950
0
19 May 2017
Negative Results in Computer Vision: A Perspective
Negative Results in Computer Vision: A Perspective
Ali Borji
173
36
0
11 May 2017
Comment on "Biologically inspired protection of deep networks from
  adversarial attacks"
Comment on "Biologically inspired protection of deep networks from adversarial attacks"
Wieland Brendel
Matthias Bethge
AAML
164
34
0
05 Apr 2017
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