Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense

2 June 2020

Papers citing "Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense"

20 / 20 papers shown

Title
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck D. Song Michael E. Houle James Bailey AAML 105 739 0 08 Jan 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 65 1,345 0 12 Dec 2017
One pixel attack for fooling deep neural networks Jiawei Su Danilo Vasconcellos Vargas Kouichi Sakurai AAML 117 2,323 0 24 Oct 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 78 1,878 0 14 Aug 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 63 283 0 13 Jul 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
Adversarial and Clean Data Are Not Twins Zhitao Gong Wenlu Wang Wei-Shinn Ku AAML 51 157 0 17 Apr 2017
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly Jiajun Lu Theerasit Issaranon David A. Forsyth GAN 84 381 0 01 Apr 2017
Biologically inspired protection of deep networks from adversarial attacks Aran Nayebi Surya Ganguli AAML 59 115 0 27 Mar 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 61 950 0 14 Feb 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 133 1,737 0 08 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 247 8,550 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 517 5,897 0 08 Jul 2016
End to End Learning for Self-Driving Cars Mariusz Bojarski D. Testa Daniel Dworakowski Bernhard Firner B. Flepp ... Urs Muller Jiakai Zhang Xin Zhang Jake Zhao Karol Zieba SSL 95 4,167 0 25 Apr 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 98 3,957 0 24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 144 4,895 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 88 3,072 0 14 Nov 2015
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection Dan Xu Elisa Ricci Yan Yan Jingkuan Song N. Sebe 168 515 0 06 Oct 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 254 19,045 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 257 14,912 1 21 Dec 2013