Papers citing 'Robustness of classifiers: from adversarial to random noise'

Title
Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning W. Nash Courtney Powell Tom Drummond N. Birbilis 60 33 0 04 Aug 2019
Adversarial Robustness via Label-Smoothing Morgane Goibert Elvis Dohmatob AAML 199 18 0 27 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature AttributionAAAI Conference on Artificial Intelligence (AAAI), 2019 Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Sai Li AAML 157 110 0 08 Jun 2019
Defending Against Universal Attacks Through Selective Feature Regeneration Tejas S. Borkar Felix Heide Lina Karam AAML 209 1 0 08 Jun 2019
Adversarial Training is a Form of Data-dependent Operator Norm Regularization Kevin Roth Yannic Kilcher Thomas Hofmann 186 13 0 04 Jun 2019
Adversarial Risk Bounds for Neural Networks through Sparsity based Compression E. Balda Arash Behboodi Niklas Koep R. Mathar AAML 149 9 0 03 Jun 2019
Cross-Domain Transferability of Adversarial PerturbationsNeural Information Processing Systems (NeurIPS), 2019 Muzammal Naseer Salman H. Khan M. H. Khan Fahad Shahbaz Khan Fatih Porikli AAML 385 169 0 28 May 2019
Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction Hosnieh Sattar Katharina Krombholz Gerard Pons-Moll Mario Fritz 3DH 189 3 0 27 May 2019
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and ChallengesACM Computing Surveys (ACM CSUR), 2019 Rob Ashmore R. Calinescu Colin Paterson AI4TS 188 131 0 10 May 2019
Adversarial Examples Are Not Bugs, They Are FeaturesNeural Information Processing Systems (NeurIPS), 2019 Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 605 1,997 0 06 May 2019
Towards Analyzing Semantic Robustness of Deep Neural Networks Abdullah Hamdi Guohao Li AAML 266 17 0 09 Apr 2019
Rallying Adversarial Techniques against Deep Learning for Network Security Joseph Clements Yuzhe Yang Ankur A Sharma Hongxin Hu Yingjie Lao AAML 148 58 0 27 Mar 2019
A geometry-inspired decision-based attack Yujia Liu Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 137 54 0 26 Mar 2019
Robust Neural Networks using Randomized Adversarial Training Alexandre Araujo Laurent Meunier Rafael Pinot Benjamin Négrevergne AAML OOD 232 36 0 25 Mar 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 425 954 0 18 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 803 2,265 0 08 Feb 2019
Theoretical evidence for adversarial robustness through randomization Rafael Pinot Laurent Meunier Alexandre Araujo H. Kashima Florian Yger Cédric Gouy-Pailler Jamal Atif AAML 227 88 0 04 Feb 2019
Adaptive Gradient for Adversarial Perturbations Generation Yatie Xiao Chi-Man Pun ODL 172 10 0 01 Feb 2019
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness Li Xiao Yijie Peng J. Hong Zewu Ke Shuhuai Yang 89 0 0 31 Jan 2019
Adversarial Examples Are a Natural Consequence of Test Error in NoiseInternational Conference on Machine Learning (ICML), 2019 Nic Ford Justin Gilmer Nicholas Carlini E. D. Cubuk AAML 323 331 0 29 Jan 2019
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks P. Langenberg E. Balda Arash Behboodi R. Mathar 178 15 0 29 Jan 2019
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers Hui Xie Xiaodong Wu Weiyu Xu R. Mudumbai AAML 122 5 0 27 Jan 2019
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain Chi Zhang Xiaohan Duan Linyuan Wang Yongli Li Bin Yan Guoen Hu Ruyuan Zhang Li Tong AAML 157 1 0 22 Dec 2018
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach Tsui-Wei Weng Pin-Yu Chen Lam M. Nguyen M. Squillante Ivan Oseledets Luca Daniel AAML 198 30 0 18 Dec 2018
Perturbation Analysis of Learning Algorithms: A Unifying Perspective on Generation of Adversarial Examples E. Balda Arash Behboodi R. Mathar AAML 122 5 0 15 Dec 2018
Defending Against Universal Perturbations With Shared Adversarial Training Chaithanya Kumar Mummadi Thomas Brox J. H. Metzen AAML 158 60 0 10 Dec 2018
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 564 301 0 03 Dec 2018
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness S. Latif R. Rana Junaid Qadir GAN AAML 149 44 0 28 Nov 2018
Generalizable Adversarial Training via Spectral NormalizationInternational Conference on Learning Representations (ICLR), 2018 Farzan Farnia Jesse M. Zhang David Tse OOD AAML 147 148 0 19 Nov 2018
Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu Jingwei Zhang Dacheng Tao AAML 126 71 0 13 Nov 2018
A Geometric Perspective on the Transferability of Adversarial DirectionsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2018 Duncan C. McElfresh H. Bidkhori Dimitris Papailiopoulos AAML 95 17 0 08 Nov 2018
Rademacher Complexity for Adversarially Robust Generalization Dong Yin Kannan Ramchandran Peter L. Bartlett AAML 347 278 0 29 Oct 2018
Evading classifiers in discrete domains with provable optimality guarantees B. Kulynych Jamie Hayes N. Samarin Carmela Troncoso AAML 184 21 0 25 Oct 2018
The Adversarial Attack and Detection under the Fisher Information Metric Chenxiao Zhao P. T. Fletcher Mixue Yu Chaomin Shen Guixu Zhang Yaxin Peng AAML 189 50 0 09 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon A. Serban E. Poll Joost Visser SILM AAML 196 49 0 02 Oct 2018
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 409 367 0 10 Sep 2018
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding Lea Schonherr Katharina Kohls Steffen Zeiler Thorsten Holz D. Kolossa AAML 159 305 0 16 Aug 2018
Out of the Black Box: Properties of deep neural networks and their applications Nizar Ouarti D. Carmona FAtt AAML 90 3 0 10 Aug 2018
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer Hsueh-Ti Derek Liu Michael Tao Chun-Liang Li Derek Nowrouzezahrai Alec Jacobson AAML 150 13 0 08 Aug 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 210 236 0 18 Jul 2018
With Friends Like These, Who Needs Adversaries? Saumya Jetley Nicholas A. Lord Juil Sock AAML 274 72 0 11 Jul 2018
A New Angle on L2 Regularization T. Tanay Lewis D. Griffin LLMSV 112 5 0 28 Jun 2018
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning J. Dhaliwal S. Shintre AAML 83 16 0 27 Jun 2018
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations T. Tanay Jerone T. A. Andrews Lewis D. Griffin 148 7 0 19 Jun 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate M. Belkin Daniel J. Hsu P. Mitra AI4CE 260 266 0 13 Jun 2018
PAC-learning in the presence of evasion adversaries Daniel Cullina A. Bhagoji Prateek Mittal AAML 177 56 0 05 Jun 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 552 1,871 0 30 May 2018
Adversarial Examples in Remote Sensing W. Czaja Neil Fendley M. Pekala Christopher R. Ratto I-J. Wang AAML 85 75 0 28 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 401 837 0 30 Apr 2018
PANDA: Facilitating Usable AI Development Jinyang Gao Wei Wang Meihui Zhang Gang Chen H. V. Jagadish Guoliang Li Teck Khim Ng Beng Chin Ooi Sheng Wang Jingren Zhou 239 4 0 26 Apr 2018