Defending Against Adversarial Examples with K-Nearest Neighbor

23 June 2019

Papers citing "Defending Against Adversarial Examples with K-Nearest Neighbor"

35 / 35 papers shown

Title
Anomalous Example Detection in Deep Learning: A Survey Saikiran Bulusu B. Kailkhura Yue Liu P. Varshney D. Song AAML 83 47 0 16 Mar 2020
Adversarial Training with Voronoi Constraints Marc Khoury Dylan Hadfield-Menell AAML 32 24 0 02 May 2019
On the Robustness of Deep K-Nearest Neighbors Chawin Sitawarin David Wagner AAML OOD 33 58 0 20 Mar 2019
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search Abhimanyu Dubey Laurens van der Maaten Zeki Yalniz Yixuan Li D. Mahajan AAML 71 64 0 05 Mar 2019
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss Nicholas Frosst Nicolas Papernot Geoffrey E. Hinton 22 159 0 05 Feb 2019
Sorting out Lipschitz function approximation Cem Anil James Lucas Roger C. Grosse 43 319 0 13 Nov 2018
Limitations of the Lipschitz constant as a defense against adversarial examples Todd P. Huster C. Chiang R. Chadha AAML 26 84 0 25 Jul 2018
Manifold Mixup: Better Representations by Interpolating Hidden States Vikas Verma Alex Lamb Christopher Beckham Amir Najafi Ioannis Mitliagkas Aaron Courville David Lopez-Paz Yoshua Bengio AAML DRL 27 35 0 13 Jun 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 36 369 0 23 May 2018
Unsupervised Representation Learning by Predicting Image Rotations Spyros Gidaris Praveer Singh N. Komodakis OOD SSL DRL 156 3,272 0 21 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 55 505 0 13 Mar 2018
L2-Nonexpansive Neural Networks Haifeng Qian M. Wegman 32 74 0 22 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 61 301 0 12 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 142 3,171 0 01 Feb 2018
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 64 734 0 08 Jan 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 55 1,335 0 12 Dec 2017
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver Thomas Hubert Julian Schrittwieser Ioannis Antonoglou Matthew Lai ... D. Kumaran T. Graepel Timothy Lillicrap Karen Simonyan Demis Hassabis 79 1,755 0 05 Dec 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 212 9,687 0 25 Oct 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 74 2,140 0 21 Aug 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 38 283 0 13 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 175 11,962 0 19 Jun 2017
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples Yizhen Wang S. Jha Kamalika Chaudhuri AAML 65 155 0 13 Jun 2017
Billion-scale similarity search with GPUs Jeff Johnson Matthijs Douze Hervé Jégou 116 3,682 0 28 Feb 2017
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 505 10,699 0 19 Feb 2017
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 70 1,735 0 24 May 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 30 4,153 0 25 Apr 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 227 10,149 0 16 Mar 2016
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 85 4,878 0 14 Nov 2015
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving Chenyi Chen Ari Seff A. Kornhauser Jianxiong Xiao 58 1,757 0 01 May 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 118 18,922 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 115 3,261 0 05 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 587 99,991 0 04 Sep 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 94 14,831 1 21 Dec 2013
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 308 16,972 0 20 Dec 2013
Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller 55 12,163 0 19 Dec 2013