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 / 151 papers shown

Title
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack Mengting Xu Tao Zhang Daoqiang Zhang AAML MedIm 21 23 0 29 Nov 2021
Adaptive Image Transformations for Transfer-based Adversarial Attack Zheng Yuan Jie Zhang Shiguang Shan OOD 24 25 0 27 Nov 2021
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 34 270 0 09 Nov 2021
LTD: Low Temperature Distillation for Robust Adversarial Training Erh-Chung Chen Che-Rung Lee AAML 27 26 0 03 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 26 14 0 02 Nov 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 36 294 0 18 Oct 2021
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting Chengyu Dong Liyuan Liu Jingbo Shang NoLa AAML 61 18 0 07 Oct 2021
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles Yuejun Guo Qiang Hu Maxime Cordy Michail Papadakis Yves Le Traon AAML 29 2 0 27 Sep 2021
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator Wenzhao Xiang Hang Su Chang-rui Liu Yandong Guo Shibao Zheng AAML 29 5 0 13 Sep 2021
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning Hong Wang Yuefan Deng Shinjae Yoo Haibin Ling Yuewei Lin AAML 32 15 0 13 Aug 2021
GoTube: Scalable Stochastic Verification of Continuous-Depth Models Sophie Gruenbacher Mathias Lechner Ramin Hasani Daniela Rus T. Henzinger S. Smolka Radu Grosu 26 17 0 18 Jul 2021
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations Nikita Muravev Aleksandr Petiushko AAML 18 7 0 28 Jun 2021
Adversarial Visual Robustness by Causal Intervention Kaihua Tang Ming Tao Hanwang Zhang CML AAML 27 21 0 17 Jun 2021
Adversarial purification with Score-based generative models Jongmin Yoon Sung Ju Hwang Juho Lee DiffM 25 153 0 11 Jun 2021
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks Jacob Mitchell Springer Melanie Mitchell Garrett Kenyon AAML 31 43 0 03 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 60 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 19 1 0 13 May 2021
LiBRe: A Practical Bayesian Approach to Adversarial Detection Zhijie Deng Xiao Yang Shizhen Xu Hang Su Jun Zhu BDL AAML 20 61 0 27 Mar 2021
Combating Adversaries with Anti-Adversaries Motasem Alfarra Juan C. Pérez Ali K. Thabet Adel Bibi Philip Torr Guohao Li AAML 34 27 0 26 Mar 2021
Adversarial Training is Not Ready for Robot Learning Mathias Lechner Ramin Hasani Radu Grosu Daniela Rus T. Henzinger AAML 38 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 36 270 0 02 Mar 2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Maura Pintor Fabio Roli Wieland Brendel Battista Biggio AAML 51 70 0 25 Feb 2021
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification Mingu Kang T. Tran Seungju Cho Daeyoung Kim AAML 27 3 0 15 Feb 2021
A Comprehensive Evaluation Framework for Deep Model Robustness Jun Guo Wei Bao Jiakai Wang Yuqing Ma Xing Gao Gang Xiao Aishan Liu Zehao Zhao Xianglong Liu Wenjun Wu AAML ELM 38 55 0 24 Jan 2021
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training Theodoros Tsiligkaridis Jay Roberts AAML 22 11 0 22 Dec 2020
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints X. Li Xiangrui Li Deng Pan D. Zhu AAML 21 17 0 14 Dec 2020
Composite Adversarial Attacks Xiaofeng Mao YueFeng Chen Shuhui Wang Hang Su Yuan He Hui Xue AAML 33 48 0 10 Dec 2020
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip Torr Guohao Li UQCV 28 34 0 08 Dec 2020
How Robust are Randomized Smoothing based Defenses to Data Poisoning? Akshay Mehra B. Kailkhura Pin-Yu Chen Jihun Hamm OOD AAML 20 32 0 02 Dec 2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan Sravanti Addepalli Arya Baburaj R. Venkatesh Babu AAML 28 92 0 30 Nov 2020
Contextual Fusion For Adversarial Robustness Aiswarya Akumalla S. Haney M. Bazhenov AAML 27 1 0 18 Nov 2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations Jinyuan Jia Binghui Wang Xiaoyu Cao Hongbin Liu Neil Zhenqiang Gong 16 24 0 15 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 17 324 0 07 Oct 2020
Query complexity of adversarial attacks Grzegorz Gluch R. Urbanke AAML 21 5 0 02 Oct 2020
Block-wise Image Transformation with Secret Key for Adversarially Robust Defense Maungmaung Aprilpyone Hitoshi Kiya 29 57 0 02 Oct 2020
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients Yifei Huang Yaodong Yu Hongyang R. Zhang Yi Ma Yuan Yao AAML 37 26 0 28 Sep 2020
Certifying Confidence via Randomized Smoothing Aounon Kumar Alexander Levine S. Feizi Tom Goldstein UQCV 33 39 0 17 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 156 0 08 Sep 2020
Stylized Adversarial Defense Muzammal Naseer Salman Khan Munawar Hayat Fahad Shahbaz Khan Fatih Porikli GAN AAML 28 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 29 30 0 14 Jul 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 26 18 0 19 May 2020
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective Linhai Ma Liang Liang AAML 16 4 0 18 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 21 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 35 12 0 11 May 2020
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning Chenglin Yang Adam Kortylewski Cihang Xie Yinzhi Cao Alan Yuille AAML 39 108 0 12 Apr 2020
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning Michael Everett Bjorn Lutjens Jonathan P. How AAML 13 41 0 11 Apr 2020
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates Amin Ghiasi Ali Shafahi Tom Goldstein 33 55 0 19 Mar 2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks Y. Tashiro Yang Song Stefano Ermon AAML 14 13 0 15 Mar 2020
Lagrangian Decomposition for Neural Network Verification Rudy Bunel Alessandro De Palma Alban Desmaison Krishnamurthy Dvijotham Pushmeet Kohli Philip Torr M. P. Kumar 19 50 0 24 Feb 2020
Towards Rapid and Robust Adversarial Training with One-Step Attacks Leo Schwinn René Raab Björn Eskofier AAML 33 6 0 24 Feb 2020