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v1v2v3 (latest)

Provable defenses against adversarial examples via the convex outer adversarial polytope

2 November 2017

Eric Wong

J. Zico Kolter

AAML

ArXiv (abs)PDF HTML Github (387★)

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

50 / 957 papers shown

Title
A Kernel Perspective for Regularizing Deep Neural Networks A. Bietti Grégoire Mialon Dexiong Chen Julien Mairal 188 15 0 30 Sep 2018
Efficient Formal Safety Analysis of Neural Networks Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 352 428 0 19 Sep 2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions Yusuke Tsuzuku Issei Sato AAML 189 65 0 11 Sep 2018
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 397 367 0 10 Sep 2018
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure Saeed Mahloujifar Dimitrios I. Diochnos Mohammad Mahmoody 208 157 0 09 Sep 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah Aleksander Madry AAML OOD 229 205 0 09 Sep 2018
Are adversarial examples inevitable? Ali Shafahi Wenjie Huang Christoph Studer Soheil Feizi Tom Goldstein SILM 385 290 0 06 Sep 2018
Lipschitz Networks and Distributional Robustness Zac Cranko Simon Kornblith Zhan Shi Richard Nock OOD 135 11 0 04 Sep 2018
Distributionally Adversarial Attack T. Zheng Changyou Chen K. Ren OOD 315 128 0 16 Aug 2018
Security and Privacy Issues in Deep Learning Ho Bae Jaehee Jang Dahuin Jung Hyemi Jang Heonseok Ha Hyungyu Lee Sungroh Yoon SILM MIACV 280 87 0 31 Jul 2018
Limitations of the Lipschitz constant as a defense against adversarial examples Todd P. Huster C. Chiang R. Chadha AAML 146 86 0 25 Jul 2018
Attack and defence in cellular decision-making: lessons from machine learning Thomas J. Rademaker Emmanuel Bengio P. Franccois AAML 101 7 0 10 Jul 2018
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees Min Wu Matthew Wicker Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 168 113 0 10 Jul 2018
Adversarial Reprogramming of Neural NetworksInternational Conference on Learning Representations (ICLR), 2018 Gamaleldin F. Elsayed Ian Goodfellow Jascha Narain Sohl-Dickstein OOD AAML 166 195 0 28 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 217 584 0 21 Jun 2018
Revisiting Adversarial Risk A. Suggala Adarsh Prasad Vaishnavh Nagarajan Pradeep Ravikumar AAML 198 20 0 07 Jun 2018
Towards Dependability Metrics for Neural Networks Chih-Hong Cheng Georg Nührenberg Chung-Hao Huang Harald Ruess Hirotoshi Yasuoka 155 46 0 06 Jun 2018
Killing four birds with one Gaussian process: the relation between different test-time attacks Kathrin Grosse M. Smith Michael Backes AAML 162 2 0 06 Jun 2018
PAC-learning in the presence of evasion adversaries Daniel Cullina A. Bhagoji Prateek Mittal AAML 173 56 0 05 Jun 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 263 463 0 31 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 540 1,871 0 30 May 2018
Training verified learners with learned verifiers Krishnamurthy Dvijotham Sven Gowal Robert Stanforth Relja Arandjelović Brendan O'Donoghue J. Uesato Pushmeet Kohli OOD 231 171 0 25 May 2018
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 363 235 0 25 May 2018
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song Rui Shu Nate Kushman Stefano Ermon GAN AAML 478 339 0 21 May 2018
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha AAML 264 22 0 20 May 2018
Curriculum Adversarial Training Qi-Zhi Cai Min Du Chang-rui Liu Basel Alomair AAML 151 180 0 13 May 2018
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems Chih-Hong Cheng Chung-Hao Huang Hirotoshi Yasuoka 86 42 0 11 May 2018
Reachability Analysis of Deep Neural Networks with Provable Guarantees Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 177 278 0 06 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
Formal Security Analysis of Neural Networks using Symbolic Intervals Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 252 506 0 28 Apr 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 278 729 0 25 Apr 2018
ADef: an Iterative Algorithm to Construct Adversarial DeformationsInternational Conference on Learning Representations (ICLR), 2018 Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 194 106 0 20 Apr 2018
Semantic Adversarial Deep LearningIEEE design & test (D&T), 2018 Sanjit A. Seshia S. Jha T. Dreossi AAML SILM 153 93 0 19 Apr 2018
Adversarial Attacks Against Medical Deep Learning Systems S. G. Finlayson Hyung Won Chung I. Kohane Andrew L. Beam SILM AAML OOD MedIm 252 250 0 15 Apr 2018
A Dual Approach to Scalable Verification of Deep Networks Krishnamurthy Dvijotham Dvijotham Robert Stanforth Sven Gowal Timothy A. Mann Pushmeet Kohli 199 407 0 17 Mar 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 296 651 0 16 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 320 545 0 13 Mar 2018
Detecting Adversarial Examples via Neural Fingerprinting Sumanth Dathathri Stephan Zheng Tianwei Yin Richard M. Murray Yisong Yue MLAU AAML 160 0 0 11 Mar 2018
Testing Deep Neural Networks Youcheng Sun Xiaowei Huang Daniel Kroening James Sharp Matthew Hill Rob Ashmore AAML 218 234 0 10 Mar 2018
On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples Mahmood Sharif Lujo Bauer Michael K. Reiter AAML 288 145 0 27 Feb 2018
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans Gamaleldin F. Elsayed Shreya Shankar Brian Cheung Nicolas Papernot Alexey Kurakin Ian Goodfellow Jascha Narain Sohl-Dickstein AAML 260 279 0 22 Feb 2018
L2-Nonexpansive Neural Networks Haifeng Qian M. Wegman 176 76 0 22 Feb 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 535 634 0 15 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 424 342 0 12 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 656 982 0 09 Feb 2018
Hardening Deep Neural Networks via Adversarial Model Cascades Deepak Vijaykeerthy Anshuman Suri S. Mehta Ponnurangam Kumaraguru AAML 149 9 0 02 Feb 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Abigail Z. Jacobs AAML 337 990 0 29 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Lin Wang AAML 433 1,980 0 02 Jan 2018
A General Framework for Adversarial Examples with Objectives Mahmood Sharif Sruti Bhagavatula Lujo Bauer Michael K. Reiter AAML GAN 214 213 0 31 Dec 2017
ReabsNet: Detecting and Revising Adversarial Examples Jiefeng Chen Zihang Meng Changtian Sun Weiliang Tang Yinglun Zhu AAML GAN 125 4 0 21 Dec 2017

v1v2v3 (latest)

Provable defenses against adversarial examples via the convex outer adversarial polytope

2 November 2017

Eric Wong

J. Zico Kolter

AAML

ArXiv (abs)PDF HTML Github (387★)

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

50 / 957 papers shown

Title
A Kernel Perspective for Regularizing Deep Neural Networks A. Bietti Grégoire Mialon Dexiong Chen Julien Mairal 188 15 0 30 Sep 2018
Efficient Formal Safety Analysis of Neural Networks Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 352 428 0 19 Sep 2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions Yusuke Tsuzuku Issei Sato AAML 189 65 0 11 Sep 2018
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 397 367 0 10 Sep 2018
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure Saeed Mahloujifar Dimitrios I. Diochnos Mohammad Mahmoody 208 157 0 09 Sep 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah Aleksander Madry AAML OOD 229 205 0 09 Sep 2018
Are adversarial examples inevitable? Ali Shafahi Wenjie Huang Christoph Studer Soheil Feizi Tom Goldstein SILM 385 290 0 06 Sep 2018
Lipschitz Networks and Distributional Robustness Zac Cranko Simon Kornblith Zhan Shi Richard Nock OOD 135 11 0 04 Sep 2018
Distributionally Adversarial Attack T. Zheng Changyou Chen K. Ren OOD 315 128 0 16 Aug 2018
Security and Privacy Issues in Deep Learning Ho Bae Jaehee Jang Dahuin Jung Hyemi Jang Heonseok Ha Hyungyu Lee Sungroh Yoon SILM MIACV 280 87 0 31 Jul 2018
Limitations of the Lipschitz constant as a defense against adversarial examples Todd P. Huster C. Chiang R. Chadha AAML 146 86 0 25 Jul 2018
Attack and defence in cellular decision-making: lessons from machine learning Thomas J. Rademaker Emmanuel Bengio P. Franccois AAML 101 7 0 10 Jul 2018
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees Min Wu Matthew Wicker Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 168 113 0 10 Jul 2018
Adversarial Reprogramming of Neural NetworksInternational Conference on Learning Representations (ICLR), 2018 Gamaleldin F. Elsayed Ian Goodfellow Jascha Narain Sohl-Dickstein OOD AAML 166 195 0 28 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 217 584 0 21 Jun 2018
Revisiting Adversarial Risk A. Suggala Adarsh Prasad Vaishnavh Nagarajan Pradeep Ravikumar AAML 198 20 0 07 Jun 2018
Towards Dependability Metrics for Neural Networks Chih-Hong Cheng Georg Nührenberg Chung-Hao Huang Harald Ruess Hirotoshi Yasuoka 155 46 0 06 Jun 2018
Killing four birds with one Gaussian process: the relation between different test-time attacks Kathrin Grosse M. Smith Michael Backes AAML 162 2 0 06 Jun 2018
PAC-learning in the presence of evasion adversaries Daniel Cullina A. Bhagoji Prateek Mittal AAML 173 56 0 05 Jun 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 263 463 0 31 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 540 1,871 0 30 May 2018
Training verified learners with learned verifiers Krishnamurthy Dvijotham Sven Gowal Robert Stanforth Relja Arandjelović Brendan O'Donoghue J. Uesato Pushmeet Kohli OOD 231 171 0 25 May 2018
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 363 235 0 25 May 2018
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song Rui Shu Nate Kushman Stefano Ermon GAN AAML 478 339 0 21 May 2018
Towards Understanding Limitations of Pixel Discretization Against Adversarial Attacks Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha AAML 264 22 0 20 May 2018
Curriculum Adversarial Training Qi-Zhi Cai Min Du Chang-rui Liu Basel Alomair AAML 151 180 0 13 May 2018
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems Chih-Hong Cheng Chung-Hao Huang Hirotoshi Yasuoka 86 42 0 11 May 2018
Reachability Analysis of Deep Neural Networks with Provable Guarantees Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 177 278 0 06 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
Formal Security Analysis of Neural Networks using Symbolic Intervals Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 252 506 0 28 Apr 2018
Towards Fast Computation of Certified Robustness for ReLU Networks Tsui-Wei Weng Huan Zhang Hongge Chen Zhao Song Cho-Jui Hsieh Duane S. Boning Inderjit S. Dhillon Luca Daniel AAML 278 729 0 25 Apr 2018
ADef: an Iterative Algorithm to Construct Adversarial DeformationsInternational Conference on Learning Representations (ICLR), 2018 Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 194 106 0 20 Apr 2018
Semantic Adversarial Deep LearningIEEE design & test (D&T), 2018 Sanjit A. Seshia S. Jha T. Dreossi AAML SILM 153 93 0 19 Apr 2018
Adversarial Attacks Against Medical Deep Learning Systems S. G. Finlayson Hyung Won Chung I. Kohane Andrew L. Beam SILM AAML OOD MedIm 252 250 0 15 Apr 2018
A Dual Approach to Scalable Verification of Deep Networks Krishnamurthy Dvijotham Dvijotham Robert Stanforth Sven Gowal Timothy A. Mann Pushmeet Kohli 199 407 0 17 Mar 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 296 651 0 16 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 320 545 0 13 Mar 2018
Detecting Adversarial Examples via Neural Fingerprinting Sumanth Dathathri Stephan Zheng Tianwei Yin Richard M. Murray Yisong Yue MLAU AAML 160 0 0 11 Mar 2018
Testing Deep Neural Networks Youcheng Sun Xiaowei Huang Daniel Kroening James Sharp Matthew Hill Rob Ashmore AAML 218 234 0 10 Mar 2018
On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples Mahmood Sharif Lujo Bauer Michael K. Reiter AAML 288 145 0 27 Feb 2018
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans Gamaleldin F. Elsayed Shreya Shankar Brian Cheung Nicolas Papernot Alexey Kurakin Ian Goodfellow Jascha Narain Sohl-Dickstein AAML 260 279 0 22 Feb 2018
L2-Nonexpansive Neural Networks Haifeng Qian M. Wegman 176 76 0 22 Feb 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 535 634 0 15 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 424 342 0 12 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 656 982 0 09 Feb 2018
Hardening Deep Neural Networks via Adversarial Model Cascades Deepak Vijaykeerthy Anshuman Suri S. Mehta Ponnurangam Kumaraguru AAML 149 9 0 02 Feb 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Abigail Z. Jacobs AAML 337 990 0 29 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Lin Wang AAML 433 1,980 0 02 Jan 2018
A General Framework for Adversarial Examples with Objectives Mahmood Sharif Sruti Bhagavatula Lujo Bauer Michael K. Reiter AAML GAN 214 213 0 31 Dec 2017
ReabsNet: Detecting and Revising Adversarial Examples Jiefeng Chen Zihang Meng Changtian Sun Weiliang Tang Yinglun Zhu AAML GAN 125 4 0 21 Dec 2017