Analysis of classifiers' robustness to adversarial perturbations

9 February 2015

Papers citing "Analysis of classifiers' robustness to adversarial perturbations"

39 / 39 papers shown

Title
A Survey of Neural Network Robustness Assessment in Image Recognition Jie Wang Jun Ai Minyan Lu Haoran Su Dan Yu Yutao Zhang Junda Zhu Jingyu Liu AAML 28 3 0 12 Apr 2024
When are Local Queries Useful for Robust Learning? Pascale Gourdeau Varun Kanade Marta Z. Kwiatkowska J. Worrell OOD 27 1 0 12 Oct 2022
GAMA: Generative Adversarial Multi-Object Scene Attacks Abhishek Aich Calvin-Khang Ta Akash Gupta Chengyu Song S. Krishnamurthy M. Salman Asif A. Roy-Chowdhury AAML 36 17 0 20 Sep 2022
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines Wen Su Qingna Li AAML 11 0 0 12 Jun 2022
Building Robust Ensembles via Margin Boosting Dinghuai Zhang Hongyang R. Zhang Aaron Courville Yoshua Bengio Pradeep Ravikumar A. Suggala AAML UQCV 35 15 0 07 Jun 2022
Attacking and Defending Deep Reinforcement Learning Policies Chao Wang AAML 20 2 0 16 May 2022
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks Pascale Gourdeau Varun Kanade Marta Z. Kwiatkowska J. Worrell AAML 11 5 0 12 May 2022
The Effects of Regularization and Data Augmentation are Class Dependent Randall Balestriero Léon Bottou Yann LeCun 28 94 0 07 Apr 2022
Attack Transferability Characterization for Adversarially Robust Multi-label Classification Zhuo Yang Yufei Han Xiangliang Zhang AAML 14 4 0 29 Jun 2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness Jie Ren Die Zhang Yisen Wang Lu Chen Zhanpeng Zhou ... Xu Cheng Xin Eric Wang Meng Zhou Jie Shi Quanshi Zhang AAML 64 22 0 12 Mar 2021
Achieving Adversarial Robustness Requires An Active Teacher Chao Ma Lexing Ying 19 1 0 14 Dec 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 41 8 0 03 Nov 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 19 48 0 19 Oct 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 17 73 0 07 Aug 2020
Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components Ken Alparslan Yigit Can Alparslan Matthew Burlick AAML 11 8 0 14 Jul 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 17 85 0 23 Jun 2020
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data Lu Wang Huan Zhang Jinfeng Yi Cho-Jui Hsieh Yuan Jiang AAML 21 12 0 11 May 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 50 63 0 02 Mar 2020
Understanding and Quantifying Adversarial Examples Existence in Linear Classification Xupeng Shi A. Ding AAML 14 3 0 27 Oct 2019
Cross-Domain Transferability of Adversarial Perturbations Muzammal Naseer Salman H. Khan M. H. Khan F. Khan Fatih Porikli AAML 19 9 0 28 May 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 21 40 0 03 Mar 2019
Stable and Fair Classification Lingxiao Huang Nisheeth K. Vishnoi FaML 19 71 0 21 Feb 2019
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks Jan Svoboda Jonathan Masci Federico Monti M. Bronstein Leonidas J. Guibas AAML GNN 33 41 0 31 May 2018
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness Carlos Lassance Vincent Gripon Antonio Ortega AAML 13 16 0 24 May 2018
Robust GANs against Dishonest Adversaries Zhi Xu Chengtao Li Stefanie Jegelka AAML 26 3 0 27 Feb 2018
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks Qi Liu Tao Liu Zihao Liu Yanzhi Wang Yier Jin Wujie Wen AAML 27 48 0 14 Feb 2018
A3T: Adversarially Augmented Adversarial Training Akram Erraqabi A. Baratin Yoshua Bengio Simon Lacoste-Julien AAML 22 9 0 12 Jan 2018
Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo Yoshua Bengio AAML 17 249 0 30 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks A. Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 33 11,825 0 19 Jun 2017
Robustness of classifiers to universal perturbations: a geometric perspective Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard Stefano Soatto AAML 21 118 0 26 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 19 796 0 28 Apr 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick D. McDaniel AAML SILM 19 554 0 11 Apr 2017
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 22 2,509 0 26 Oct 2016
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 178 931 0 21 Oct 2016
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches E. Rodner Marcel Simon Robert B. Fisher Joachim Denzler 13 39 0 21 Oct 2016
Towards Verified Artificial Intelligence S. Seshia Dorsa Sadigh S. Shankar Sastry 13 203 0 27 Jun 2016
Exploring the Space of Adversarial Images Pedro Tabacof Eduardo Valle AAML 12 191 0 19 Oct 2015
Improving Back-Propagation by Adding an Adversarial Gradient Arild Nøkland AAML 19 32 0 14 Oct 2015
Evasion and Hardening of Tree Ensemble Classifiers Alex Kantchelian J. D. Tygar A. Joseph AAML 6 206 0 25 Sep 2015