Adversarial Examples Are Not Bugs, They Are Features

6 May 2019

Papers citing "Adversarial Examples Are Not Bugs, They Are Features"

50 / 373 papers shown

Title
Data-driven Identification of 2D Partial Differential Equations using extracted physical features Kazem Meidani A. Farimani 21 17 0 20 Oct 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 31 48 0 19 Oct 2020
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial Attack Hui Liu Bo Zhao Minzhi Ji Peng Liu AAML 29 6 0 14 Oct 2020
A Unified Approach to Interpreting and Boosting Adversarial Transferability Xin Eric Wang Jie Ren Shuyu Lin Xiangming Zhu Yisen Wang Quanshi Zhang AAML 29 94 0 08 Oct 2020
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability Ishai Rosenberg Shai Meir J. Berrebi I. Gordon Guillaume Sicard Eli David AAML SILM 11 25 0 28 Sep 2020
Adversarial Training with Stochastic Weight Average Joong-won Hwang Youngwan Lee Sungchan Oh Yuseok Bae OOD AAML 29 11 0 21 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 43 62 0 11 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 157 0 08 Sep 2020
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks Wei-An Lin Chun Pong Lau Alexander Levine Ramalingam Chellappa S. Feizi AAML 81 60 0 05 Sep 2020
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning Martin Mundt Yongjun Hong Iuliia Pliushch Visvanathan Ramesh CLL 30 146 0 03 Sep 2020
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training Alfred Laugros A. Caplier Matthieu Ospici AAML 24 19 0 19 Aug 2020
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 21 3 0 07 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 29 73 0 07 Aug 2020
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging N. Arun N. Gaw P. Singh Ken Chang M. Aggarwal ... J. Patel M. Gidwani Julius Adebayo M. D. Li Jayashree Kalpathy-Cramer FAtt 30 109 0 06 Aug 2020
When is invariance useful in an Out-of-Distribution Generalization problem ? Masanori Koyama Shoichiro Yamaguchi OOD 34 65 0 04 Aug 2020
Robust and Generalizable Visual Representation Learning via Random Convolutions Zhenlin Xu Deyi Liu Junlin Yang Colin Raffel Marc Niethammer OOD AAML 53 191 0 25 Jul 2020
Adversarial Training Reduces Information and Improves Transferability M. Terzi Alessandro Achille Marco Maggipinto Gian Antonio Susto AAML 24 23 0 22 Jul 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 37 417 0 16 Jul 2020
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations Chaoning Zhang Philipp Benz Tooba Imtiaz In-So Kweon SSL AAML 22 118 0 13 Jul 2020
Boundary thickness and robustness in learning models Yaoqing Yang Rekha Khanna Yaodong Yu A. Gholami Kurt Keutzer Joseph E. Gonzalez Kannan Ramchandran Michael W. Mahoney OOD 18 37 0 09 Jul 2020
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency Robert Geirhos Kristof Meding Felix Wichmann 19 117 0 30 Jun 2020
Overcoming Statistical Shortcuts for Open-ended Visual Counting Corentin Dancette Rémi Cadène Xinlei Chen Matthieu Cord 13 3 0 17 Jun 2020
Self-training Avoids Using Spurious Features Under Domain Shift Yining Chen Colin Wei Ananya Kumar Tengyu Ma OOD 29 85 0 17 Jun 2020
Noise or Signal: The Role of Image Backgrounds in Object Recognition Kai Y. Xiao Logan Engstrom Andrew Ilyas A. Madry 25 377 0 17 Jun 2020
Adversarial Self-Supervised Contrastive Learning Minseon Kim Jihoon Tack Sung Ju Hwang SSL 28 247 0 13 Jun 2020
Calibrated neighborhood aware confidence measure for deep metric learning Maryna Karpusha Sunghee Yun István Fehérvári UQCV FedML 27 2 0 08 Jun 2020
A Self-supervised Approach for Adversarial Robustness Muzammal Naseer Salman Khan Munawar Hayat Fahad Shahbaz Khan Fatih Porikli AAML 22 251 0 08 Jun 2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks Arash Rahnama A.-Yu Tseng FAtt AAML FaML 19 5 0 20 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 39 147 0 20 May 2020
Blind Backdoors in Deep Learning Models Eugene Bagdasaryan Vitaly Shmatikov AAML FedML SILM 46 298 0 08 May 2020
Towards Frequency-Based Explanation for Robust CNN Zifan Wang Yilin Yang Ankit Shrivastava Varun Rawal Zihao Ding AAML FAtt 21 47 0 06 May 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 31 8 0 23 Apr 2020
Provably robust deep generative models Filipe Condessa Zico Kolter AAML OOD 11 5 0 22 Apr 2020
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning Hongcai Xu J. Bao Gaojie Zhang 27 8 0 19 Apr 2020
M2m: Imbalanced Classification via Major-to-minor Translation Jaehyung Kim Jongheon Jeong Jinwoo Shin 17 220 0 01 Apr 2020
Going in circles is the way forward: the role of recurrence in visual inference R. S. V. Bergen N. Kriegeskorte 17 82 0 26 Mar 2020
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection Mohammadreza Salehi Atrin Arya Barbod Pajoum Mohammad Otoofi Amirreza Shaeiri M. Rohban Hamid R. Rabiee AAML 29 62 0 12 Mar 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 163 113 0 05 Mar 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 215 907 0 02 Mar 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 33 397 0 26 Feb 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 37 69 0 25 Feb 2020
Gödel's Sentence Is An Adversarial Example But Unsolvable Xiaodong Qi Lansheng Han AAML 30 0 0 25 Feb 2020
CEB Improves Model Robustness Ian S. Fischer Alexander A. Alemi AAML 19 28 0 13 Feb 2020
The Conditional Entropy Bottleneck Ian S. Fischer OOD 29 116 0 13 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 38 64 0 11 Feb 2020
Efficient Adversarial Training with Transferable Adversarial Examples Haizhong Zheng Ziqi Zhang Juncheng Gu Honglak Lee A. Prakash AAML 24 108 0 27 Dec 2019
Label-Consistent Backdoor Attacks Alexander Turner Dimitris Tsipras A. Madry AAML 11 383 0 05 Dec 2019
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds Abdullah Hamdi Sara Rojas Ali K. Thabet Guohao Li AAML 3DPC 33 127 0 01 Dec 2019
Universal adversarial examples in speech command classification Jon Vadillo Roberto Santana AAML 34 29 0 22 Nov 2019
Defective Convolutional Networks Tiange Luo Tianle Cai Mengxiao Zhang Siyu Chen Di He Liwei Wang AAML 30 3 0 19 Nov 2019