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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

1 February 2018

Papers citing "Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples"

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Understanding Robustness in Teacher-Student Setting: A New Perspective Zhuolin Yang Zhaoxi Chen Tiffany Cai Xinyun Chen Yue Liu Yuandong Tian AAML 55 2 0 25 Feb 2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Maura Pintor Fabio Roli Wieland Brendel Battista Biggio AAML 92 73 0 25 Feb 2021
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis Leo Schwinn A. Nguyen René Raab Leon Bungert Daniel Tenbrinck Dario Zanca Martin Burger Bjoern M. Eskofier AAML 42 16 0 24 Feb 2021
Multiplicative Reweighting for Robust Neural Network Optimization Noga Bar Tomer Koren Raja Giryes OOD NoLa 85 9 0 24 Feb 2021
Automated Discovery of Adaptive Attacks on Adversarial Defenses Chengyuan Yao Pavol Bielik Petar Tsankov Martin Vechev AAML 99 24 0 23 Feb 2021
On the robustness of randomized classifiers to adversarial examples Rafael Pinot Laurent Meunier Florian Yger Cédric Gouy-Pailler Y. Chevaleyre Jamal Atif AAML 75 14 0 22 Feb 2021
Effective and Efficient Vote Attack on Capsule Networks Jindong Gu Baoyuan Wu Volker Tresp AAML 70 27 0 19 Feb 2021
Center Smoothing: Certified Robustness for Networks with Structured Outputs Aounon Kumar Tom Goldstein OOD AAML UQCV 84 19 0 19 Feb 2021
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids Jiangnan Li Yingyuan Yang Jinyuan Stella Sun K. Tomsovic Hairong Qi AAML 127 15 0 17 Feb 2021
Bridging the Gap Between Adversarial Robustness and Optimization Bias Fartash Faghri Sven Gowal C. N. Vasconcelos David J. Fleet Fabian Pedregosa Nicolas Le Roux AAML 234 7 0 17 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs Soheil Feizi AAML 102 47 0 15 Feb 2021
Certifiably Robust Variational Autoencoders Ben Barrett A. Camuto M. Willetts Tom Rainforth AAML DRL 88 17 0 15 Feb 2021
Data Quality Matters For Adversarial Training: An Empirical Study Chengyu Dong Liyuan Liu Jingbo Shang AAML 59 10 0 15 Feb 2021
Generating Structured Adversarial Attacks Using Frank-Wolfe Method Ehsan Kazemi Thomas Kerdreux Liquang Wang AAML DiffM 53 1 0 15 Feb 2021
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification Mingu Kang T. Tran Seungju Cho Daeyoung Kim AAML 49 3 0 15 Feb 2021
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 95 138 0 14 Feb 2021
Perceptually Constrained Adversarial Attacks Muhammad Zaid Hameed András Gyorgy 60 12 0 14 Feb 2021
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective Chaoning Zhang Philipp Benz Adil Karjauv In So Kweon AAML 97 42 0 12 Feb 2021
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons Bohang Zhang Tianle Cai Zhou Lu Di He Liwei Wang OOD 92 51 0 10 Feb 2021
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection Hanshu Yan Jingfeng Zhang Gang Niu Jiashi Feng Vincent Y. F. Tan Masashi Sugiyama AAML 49 42 0 10 Feb 2021
Bayesian Inference with Certifiable Adversarial Robustness Matthew Wicker Luca Laurenti A. Patané Zhoutong Chen Zheng Zhang Marta Z. Kwiatkowska AAML BDL 142 30 0 10 Feb 2021
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis Fengting Li Xuankai Liu Xiaoli Zhang Qi Li Kun Sun Kang Li AAML 73 13 0 10 Feb 2021
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models Sahar Abdelnabi Mario Fritz AAML 44 7 0 09 Feb 2021
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples Jay Nandy Sudipan Saha Wynne Hsu Mong Li Lee Xiaosu Zhu AAML 82 4 0 09 Feb 2021
Target Training Does Adversarial Training Without Adversarial Samples Blerta Lindqvist AAML 25 0 0 09 Feb 2021
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 143 73 0 09 Feb 2021
Efficient Certified Defenses Against Patch Attacks on Image Classifiers J. H. Metzen Maksym Yatsura AAML 61 41 0 08 Feb 2021
Meta-Learning with Neural Tangent Kernels Yufan Zhou Zhenyi Wang Jiayi Xian Changyou Chen Jinhui Xu 62 20 0 07 Feb 2021
Adversarial Imaging Pipelines Buu Phan Fahim Mannan Felix Heide AAML 56 26 0 07 Feb 2021
Noise Optimization for Artificial Neural Networks Li Xiao Zeliang Zhang Yijie Peng 113 14 0 06 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan Kankanhalli Masashi Sugiyama AAML 97 27 0 06 Feb 2021
Robust Single-step Adversarial Training with Regularizer Lehui Xie Yaopeng Wang Jianwei Yin Ximeng Liu AAML 59 1 0 05 Feb 2021
Optimal Transport as a Defense Against Adversarial Attacks Quentin Bouniot Romaric Audigier Angélique Loesch AAML OOD 32 9 0 05 Feb 2021
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks Chong Xiang Prateek Mittal AAML 113 53 0 05 Feb 2021
Adversarially Robust Learning with Unknown Perturbation Sets Omar Montasser Steve Hanneke Nathan Srebro AAML 85 28 0 03 Feb 2021
IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks Yixiang Wang Jiqiang Liu Xiaolin Chang J. Misic Vojislav B. Mišić AAML 69 12 0 03 Feb 2021
Fast Training of Provably Robust Neural Networks by SingleProp Akhilan Boopathy Tsui-Wei Weng Sijia Liu Pin-Yu Chen Gaoyuan Zhang Luca Daniel AAML 57 7 0 01 Feb 2021
Admix: Enhancing the Transferability of Adversarial Attacks Xiaosen Wang Xu He Jingdong Wang Kun He AAML 153 201 0 31 Jan 2021
Adversarial Learning with Cost-Sensitive Classes Hao Shen Sihong Chen Ran Wang Xizhao Wang AAML 77 11 0 29 Jan 2021
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems Utku Ozbulak Baptist Vandersmissen A. Jalalvand Ivo Couckuyt Arnout Van Messem W. D. Neve AAML 31 19 0 26 Jan 2021
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning Anh-Vu Bui Trung Le He Zhao Paul Montague S. Çamtepe Dinh Q. Phung AAML 53 14 0 25 Jan 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 97 61 0 24 Jan 2021
Error Diffusion Halftoning Against Adversarial Examples Shao-Yuan Lo Vishal M. Patel DiffM 61 4 0 23 Jan 2021
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving James Tu Huichen Li Xinchen Yan Mengye Ren Yun Chen Ming Liang E. Bitar Ersin Yumer R. Urtasun AAML 91 78 0 17 Jan 2021
Removing Undesirable Feature Contributions Using Out-of-Distribution Data Saehyung Lee Changhwa Park Hyungyu Lee Jihun Yi Jonghyun Lee Sungroh Yoon OODD 102 26 0 17 Jan 2021
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks Jia-Wei Liu Yaochu Jin AAML OOD 75 38 0 16 Jan 2021
Fundamental Tradeoffs in Distributionally Adversarial Training M. Mehrabi Adel Javanmard Ryan A. Rossi Anup B. Rao Tung Mai AAML 55 18 0 15 Jan 2021
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks Junyoung Byun Hyojun Go Changick Kim AAML 193 21 0 13 Jan 2021
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing Andreas Bär Jonas Löhdefink Nikhil Kapoor Serin Varghese Fabian Hüger Peter Schlicht Tim Fingscheidt AAML 192 35 0 11 Jan 2021
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning Olakunle Ibitoye M. O. Shafiq Ashraf Matrawy FedML 55 19 0 08 Jan 2021