v1v2v3 (latest)

Provable defenses against adversarial examples via the convex outer adversarial polytope

2 November 2017

Eric Wong

J. Zico Kolter

AAML

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Papers citing "Provable defenses against adversarial examples via the convex outer adversarial polytope"

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Title
$Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$$ Provable robustness against all adversarial $l_p$ -perturbations for $p\geq 1$ International Conference on Learning Representations (ICLR), 2019 Francesco Croce Matthias Hein OOD 145 77 0 27 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial ExamplesConference on Computer and Communications Security (CCS), 2019 Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 175 275 0 24 May 2019
Robust Attribution RegularizationNeural Information Processing Systems (NeurIPS), 2019 Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha OOD 164 88 0 23 May 2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-EncoderNeural Information Processing Systems (NeurIPS), 2019 Ji Feng Qi-Zhi Cai Zhi Zhou AAML 123 113 0 22 May 2019
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep LearningUSENIX Annual Technical Conference (USENIX ATC), 2019 Z. Din P. Tigas Samuel T. King B. Livshits VLM 276 32 0 17 May 2019
Misleading Failures of Partial-input Baselines Shi Feng Eric Wallace Jordan L. Boyd-Graber 211 0 0 14 May 2019
Harnessing the Vulnerability of Latent Layers in Adversarially Trained ModelsInternational Joint Conference on Artificial Intelligence (IJCAI), 2019 M. Singh Abhishek Sinha Nupur Kumari Harshitha Machiraju Balaji Krishnamurthy V. Balasubramanian AAML 183 66 0 13 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial ExamplesACM International Conference on Embedded Networked Sensor Systems (SenSys), 2019 Qun Song Zhenyu Yan Rui Tan AAML 122 25 0 11 May 2019
Does Data Augmentation Lead to Positive Margin?International Conference on Machine Learning (ICML), 2019 Shashank Rajput Zhili Feng Zachary B. Charles Po-Ling Loh Dimitris Papailiopoulos 152 39 0 08 May 2019
Adaptive Generation of Unrestricted Adversarial Inputs Isaac Dunn Hadrien Pouget T. Melham Daniel Kroening AAML 136 7 0 07 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
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 193 26 0 05 May 2019
Adversarial Training with Voronoi Constraints Marc Khoury Dylan Hadfield-Menell AAML 132 24 0 02 May 2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural NetworksInternational Conference on Machine Learning (ICML), 2019 Yandong Li Lijun Li Liqiang Wang Tong Zhang Boqing Gong AAML 234 261 0 01 May 2019
Adversarial Training and Robustness for Multiple PerturbationsNeural Information Processing Systems (NeurIPS), 2019 Florian Tramèr Dan Boneh AAML SILM 425 410 0 30 Apr 2019
Adversarial Training for Free!Neural Information Processing Systems (NeurIPS), 2019 Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 581 1,362 0 29 Apr 2019
Distributed generation of privacy preserving data with user customization Xiao Chen Thomas Navidi Stefano Ermon Ram Rajagopal 146 11 0 20 Apr 2019
Reward Potentials for Planning with Learned Neural Network Transition Models B. Say Scott Sanner Sylvie Thiébaux 141 4 0 19 Apr 2019
Gotta Catch Ém All: Using Honeypots to Catch Adversarial Attacks on Neural Networks Shawn Shan Emily Wenger Bolun Wang Yangqiu Song Haitao Zheng Ben Y. Zhao 285 78 0 18 Apr 2019
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks David J. Miller Zhen Xiang G. Kesidis AAML 165 37 0 12 Apr 2019
The coupling effect of Lipschitz regularization in deep neural networks Nicolas P. Couellan 107 5 0 12 Apr 2019
Universal Lipschitz Approximation in Bounded Depth Neural Networks Jérémy E. Cohen Todd P. Huster Ravid Cohen AAML 107 23 0 09 Apr 2019
On Training Robust PDF Malware Classifiers Yizheng Chen Shiqi Wang Dongdong She Suman Jana AAML 181 75 0 06 Apr 2019
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks Yinpeng Dong Tianyu Pang Hang Su Jun Zhu SILM AAML 221 992 0 05 Apr 2019
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks Fatemeh Sheikholeslami Swayambhoo Jain G. Giannakis AAML 180 25 0 05 Apr 2019
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks Aamir Mustafa Salman Khan Munawar Hayat Roland Göcke Jianbing Shen Ling Shao AAML 248 157 0 01 Apr 2019
A Provable Defense for Deep Residual Networks M. Mirman Gagandeep Singh Martin Vechev 131 28 0 29 Mar 2019
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks Francesco Croce Jonas Rauber Matthias Hein AAML 116 32 0 27 Mar 2019
Defending against Whitebox Adversarial Attacks via Randomized Discretization Yuchen Zhang Abigail Z. Jacobs AAML 180 77 0 25 Mar 2019
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness J. Jacobsen Jens Behrmann Nicholas Carlini Florian Tramèr Nicolas Papernot AAML 129 47 0 25 Mar 2019
The LogBarrier adversarial attack: making effective use of decision boundary information Chris Finlay Aram-Alexandre Pooladian Adam M. Oberman AAML 142 29 0 25 Mar 2019
A Formalization of Robustness for Deep Neural Networks T. Dreossi Shromona Ghosh Alberto L. Sangiovanni-Vincentelli Sanjit A. Seshia GAN 122 32 0 24 Mar 2019
Scalable Differential Privacy with Certified Robustness in Adversarial Learning Nhathai Phan My T. Thai Han Hu R. Jin Tong Sun Dejing Dou 380 14 0 23 Mar 2019
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of PolytopesNeural Information Processing Systems (NeurIPS), 2019 Matt Jordan Justin Lewis A. Dimakis AAML 170 59 0 20 Mar 2019
Algorithms for Verifying Deep Neural Networks Changliu Liu Tomer Arnon Christopher Lazarus Christopher A. Strong Clark W. Barrett Mykel J. Kochenderfer AAML 257 440 0 15 Mar 2019
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models Paul Michel Xian Li Graham Neubig J. Pino AAML 166 142 0 15 Mar 2019
On Certifying Non-uniform Bound against Adversarial Attacks Chen Liu Ryota Tomioka Volkan Cevher AAML 138 19 0 15 Mar 2019
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks Ian Goodfellow AAML OOD 153 33 0 14 Mar 2019
Semantics Preserving Adversarial Learning Ousmane Amadou Dia Elnaz Barshan Reza Babanezhad AAML GAN 382 2 0 10 Mar 2019
Detecting Overfitting via Adversarial ExamplesNeural Information Processing Systems (NeurIPS), 2019 Roman Werpachowski András Gyorgy Csaba Szepesvári TDI 203 45 0 06 Mar 2019
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite ProgrammingIEEE Transactions on Automatic Control (IEEE TAC), 2019 Mahyar Fazlyab M. Morari George J. Pappas AAML 259 253 0 04 Mar 2019
A Fundamental Performance Limitation for Adversarial ClassificationIEEE Control Systems Letters (L-CSS), 2019 Abed AlRahman Al Makdah Vaibhav Katewa Fabio Pasqualetti AAML 148 9 0 04 Mar 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial PerturbationsComputer Vision and Pattern Recognition (CVPR), 2019 Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 183 44 0 03 Mar 2019
Robust Decision Trees Against Adversarial ExamplesInternational Conference on Machine Learning (ICML), 2019 Hongge Chen Huan Zhang Duane S. Boning Cho-Jui Hsieh AAML 292 124 0 27 Feb 2019
Architecting Dependable Learning-enabled Autonomous Systems: A Survey Chih-Hong Cheng Dhiraj Gulati Rongjie Yan 109 4 0 27 Feb 2019
Disentangled Deep Autoencoding Regularization for Robust Image Classification Zhenyu Duan Martin Renqiang Min Erran L. Li Mingbo Cai Yi Tian Xu Bingbing Ni 88 2 0 27 Feb 2019
Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster VerificationSensors Applications Symposium (SAS), 2019 Jianlin Li Pengfei Yang Jiangchao Liu Liqian Chen Xiaowei Huang Lijun Zhang AAML 145 83 0 26 Feb 2019
Verification of Non-Linear Specifications for Neural Networks Chongli Qin Krishnamurthy Dvijotham Dvijotham Brendan O'Donoghue Rudy Bunel Robert Stanforth Sven Gowal J. Uesato G. Swirszcz Pushmeet Kohli AAML 143 45 0 25 Feb 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks Hadi Salman Greg Yang Huan Zhang Cho-Jui Hsieh Pengchuan Zhang AAML 447 280 0 23 Feb 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions G. Ding Kry Yik-Chau Lui Xiaomeng Jin Luyu Wang Ruitong Huang OOD 115 64 0 22 Feb 2019