Adversarial Risk and the Dangers of Evaluating Against Weak Attacks

15 February 2018

Papers citing "Adversarial Risk and the Dangers of Evaluating Against Weak Attacks"

50 / 108 papers shown

Title
Adversarial Visual Robustness by Causal Intervention Kaihua Tang Ming Tao Hanwang Zhang CML AAML 19 21 0 17 Jun 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 14 58 0 21 May 2021
What Clinical Trials Can Teach Us about the Development of More Resilient AI for Cybersecurity Edmon Begoli Robert A. Bridges Sean Oesch Kathryn Knight 9 1 0 13 May 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 24 34 0 15 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 25 268 0 02 Mar 2021
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification Mingu Kang T. Tran Seungju Cho Daeyoung Kim AAML 21 3 0 15 Feb 2021
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training Theodoros Tsiligkaridis Jay Roberts AAML 9 11 0 22 Dec 2020
Composite Adversarial Attacks Xiaofeng Mao YueFeng Chen Shuhui Wang Hang Su Yuan He Hui Xue AAML 25 47 0 10 Dec 2020
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip H. S. Torr Bernard Ghanem UQCV 23 34 0 08 Dec 2020
Contextual Fusion For Adversarial Robustness Aiswarya Akumalla S. Haney M. Bazhenov AAML 22 1 0 18 Nov 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 323 0 07 Oct 2020
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients Yifei Huang Yaodong Yu Hongyang R. Zhang Yi-An Ma Yuan Yao AAML 29 26 0 28 Sep 2020
Certifying Confidence via Randomized Smoothing Aounon Kumar Alexander Levine S. Feizi Tom Goldstein UQCV 28 38 0 17 Sep 2020
Stylized Adversarial Defense Muzammal Naseer Salman Khan Munawar Hayat F. Khan Fatih Porikli GAN AAML 20 16 0 29 Jul 2020
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders A. Camuto M. Willetts Stephen J. Roberts Chris Holmes Tom Rainforth AAML DRL 21 29 0 14 Jul 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 18 18 0 19 May 2020
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition Tianhang Zheng Sheng Liu Changyou Chen Junsong Yuan Baochun Li K. Ren AAML 19 17 0 14 May 2020
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data Lu Wang Huan Zhang Jinfeng Yi Cho-Jui Hsieh Yuan Jiang AAML 24 12 0 11 May 2020
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning Michael Everett Bjorn Lutjens Jonathan P. How AAML 11 41 0 11 Apr 2020
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates Amin Ghiasi Ali Shafahi Tom Goldstein 23 55 0 19 Mar 2020
Lagrangian Decomposition for Neural Network Verification Rudy Bunel Alessandro De Palma Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip H. S. Torr M. P. Kumar 6 50 0 24 Feb 2020
Towards Rapid and Robust Adversarial Training with One-Step Attacks Leo Schwinn René Raab Björn Eskofier AAML 25 6 0 24 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 78 820 0 19 Feb 2020
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence S. Raschka Joshua Patterson Corey J. Nolet AI4CE 16 482 0 12 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 40 1,158 0 12 Jan 2020
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction Yantao Lu Yunhan Jia Jianyu Wang Bai Li Weiheng Chai Lawrence Carin Senem Velipasalar AAML 16 81 0 22 Nov 2019
Fine-grained Synthesis of Unrestricted Adversarial Examples Omid Poursaeed Tianxing Jiang Yordanos Goshu Harry Yang Serge J. Belongie Ser-Nam Lim AAML 30 13 0 20 Nov 2019
Adversarial Examples in Modern Machine Learning: A Review R. Wiyatno Anqi Xu Ousmane Amadou Dia A. D. Berker AAML 13 103 0 13 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 24 68 0 06 Nov 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble Huan Zhang Minhao Cheng Cho-Jui Hsieh 25 9 0 31 Oct 2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing Sven Gowal J. Uesato Chongli Qin Po-Sen Huang Timothy A. Mann Pushmeet Kohli AAML 37 88 0 21 Oct 2019
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Rémi Bernhard Pierre-Alain Moëllic J. Dutertre AAML MQ 22 18 0 27 Sep 2019
Saccader: Improving Accuracy of Hard Attention Models for Vision Gamaleldin F. Elsayed Simon Kornblith Quoc V. Le VLM 25 70 0 20 Aug 2019
Density estimation in representation space to predict model uncertainty Tiago Ramalho M. Corbalan UQCV BDL 6 37 0 20 Aug 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 18 36 0 27 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 22 535 0 09 Jun 2019
Multi-way Encoding for Robustness Donghyun Kim Sarah Adel Bargal Jianming Zhang Stan Sclaroff AAML 10 2 0 05 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 13 63 0 28 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 8 77 0 27 May 2019
Adversarial Policies: Attacking Deep Reinforcement Learning Adam Gleave Michael Dennis Cody Wild Neel Kant Sergey Levine Stuart J. Russell AAML 25 348 0 25 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 11 5 0 19 May 2019
Detecting Overfitting via Adversarial Examples Roman Werpachowski András Gyorgy Csaba Szepesvári TDI 15 45 0 06 Mar 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 28 40 0 03 Mar 2019
Defending Against Universal Perturbations With Shared Adversarial Training Chaithanya Kumar Mummadi Thomas Brox J. H. Metzen AAML 15 60 0 10 Dec 2018
MMA Training: Direct Input Space Margin Maximization through Adversarial Training G. Ding Yash Sharma Kry Yik-Chau Lui Ruitong Huang AAML 16 270 0 06 Dec 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 28 392 0 19 Nov 2018
Theoretical Analysis of Adversarial Learning: A Minimax Approach Zhuozhuo Tu Jingwei Zhang Dacheng Tao AAML 11 68 0 13 Nov 2018
Logit Pairing Methods Can Fool Gradient-Based Attacks Marius Mosbach Maksym Andriushchenko T. A. Trost Matthias Hein Dietrich Klakow AAML 17 82 0 29 Oct 2018
Combinatorial Attacks on Binarized Neural Networks Elias Boutros Khalil Amrita Gupta B. Dilkina AAML 35 40 0 08 Oct 2018
Unrestricted Adversarial Examples Tom B. Brown Nicholas Carlini Chiyuan Zhang Catherine Olsson Paul Christiano Ian Goodfellow AAML 16 101 0 22 Sep 2018