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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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Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 91 71 0 24 Jul 2021
On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang Linyi Li Xiaojun Xu B. Kailkhura Tao Xie Yue Liu AAML 106 50 0 22 Jul 2021
Improved Text Classification via Contrastive Adversarial Training Lin Pan Chung-Wei Hang Avirup Sil Saloni Potdar AAML 67 92 0 21 Jul 2021
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients Huimin Wu Zhengmian Hu Bin Gu AAML 37 9 0 21 Jul 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 108 24 0 20 Jul 2021
GoTube: Scalable Stochastic Verification of Continuous-Depth Models Sophie Gruenbacher Mathias Lechner Ramin Hasani Daniela Rus T. Henzinger S. Smolka Radu Grosu 68 17 0 18 Jul 2021
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 281 422 0 14 Jul 2021
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning Yihao Huang Qing Guo Felix Juefei Xu Lei Ma Weikai Miao Yang Liu G. Pu AAML 63 15 0 14 Jul 2021
AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense Duhun Hwang Eunjung Lee Wonjong Rhee AAML 221 16 0 14 Jul 2021
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising Anouar Kherchouche Sid Ahmed Fezza W. Hamidouche AAML 72 9 0 12 Jul 2021
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks Andrei-Șerban Ilie Marius Popescu Alin Stefanescu AAML 82 6 0 12 Jul 2021
A Closer Look at the Adversarial Robustness of Information Bottleneck Models I. Korshunova David Stutz Alexander A. Alemi Olivia Wiles Sven Gowal 56 3 0 12 Jul 2021
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models Daniel Park Haidar Khan Azer Khan Alex Gittens B. Yener AAML 37 1 0 08 Jul 2021
Understanding Intrinsic Robustness Using Label Uncertainty Xiao Zhang David Evans OOD 52 5 0 07 Jul 2021
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients Alessandro Cappelli Julien Launay Laurent Meunier Ruben Ohana Iacopo Poli AAML 53 4 0 06 Jul 2021
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving Takami Sato Qi Alfred Chen AAML ELM 79 6 0 06 Jul 2021
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization Sungyoon Lee Hoki Kim Jaewook Lee AAML 84 55 0 06 Jul 2021
Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges B. Xi AAML 49 29 0 30 Jun 2021
The Values Encoded in Machine Learning Research Abeba Birhane Pratyusha Kalluri Dallas Card William Agnew Ravit Dotan Michelle Bao 91 295 0 29 Jun 2021
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent Oliver Bryniarski Nabeel Hingun Pedro Pachuca Vincent Wang Nicholas Carlini AAML 82 37 0 28 Jun 2021
Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations Nikita Muravev Aleksandr Petiushko AAML 58 8 0 28 Jun 2021
The Feasibility and Inevitability of Stealth Attacks I. Tyukin D. Higham Alexander Bastounis Eliyas Woldegeorgis Alexander N. Gorban AAML 48 19 0 26 Jun 2021
Multi-stage Optimization based Adversarial Training Xiaosen Wang Chuanbiao Song Liwei Wang Kun He AAML 31 5 0 26 Jun 2021
Countering Adversarial Examples: Combining Input Transformation and Noisy Training Cheng Zhang Pan Gao AAML 41 3 0 25 Jun 2021
Reachability Analysis of Convolutional Neural Networks Xiaodong Yang Tomoya Yamaguchi Hoang-Dung Tran Bardh Hoxha Taylor T. Johnson Danil Prokhorov FAtt 50 6 0 22 Jun 2021
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations Sungmin Cha Naeun Ko Young Joon Yoo Taesup Moon AAML 51 2 0 22 Jun 2021
Delving into the pixels of adversarial samples Blerta Lindqvist AAML 37 0 0 21 Jun 2021
Accumulative Poisoning Attacks on Real-time Data Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu 81 20 0 18 Jun 2021
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples Maura Pintor Christian Scano Angelo Sotgiu Ambra Demontis Nicholas Carlini Battista Biggio Fabio Roli AAML 88 28 0 18 Jun 2021
Adversarial Visual Robustness by Causal Intervention Kaihua Tang Ming Tao Hanwang Zhang CML AAML 85 21 0 17 Jun 2021
Localized Uncertainty Attacks Ousmane Amadou Dia Theofanis Karaletsos C. Hazirbas Cristian Canton Ferrer I. Kabul E. Meijer AAML 55 2 0 17 Jun 2021
Towards Adversarial Robustness via Transductive Learning Jiefeng Chen Yang Guo Xi Wu Tianqi Li Qicheng Lao Yingyu Liang S. Jha AAML 48 5 0 15 Jun 2021
Probabilistic Margins for Instance Reweighting in Adversarial Training Qizhou Wang Feng Liu Bo Han Tongliang Liu Chen Gong Gang Niu Mingyuan Zhou Masashi Sugiyama AAML 86 65 0 15 Jun 2021
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers Carl-Johann Simon-Gabriel N. Sheikh Andreas Krause SILM AAML 54 3 0 14 Jun 2021
Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks Utku Ozbulak Esla Timothy Anzaku W. D. Neve Arnout Van Messem AAML 148 10 0 14 Jun 2021
Inverting Adversarially Robust Networks for Image Synthesis Renan A. Rojas-Gomez Raymond A. Yeh Minh Do A. Nguyen 52 5 0 13 Jun 2021
CARTL: Cooperative Adversarially-Robust Transfer Learning Dian Chen Hongxin Hu Qian Wang Yinli Li Cong Wang Chao Shen Qi Li 48 14 0 12 Jun 2021
TDGIA:Effective Injection Attacks on Graph Neural Networks Xu Zou Qinkai Zheng Yuxiao Dong Xinyu Guan Evgeny Kharlamov Jialiang Lu Jie Tang AAML 93 107 0 12 Jun 2021
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks Nezihe Merve Gürel Xiangyu Qi Luka Rimanic Ce Zhang Yue Liu AAML 67 39 0 11 Jun 2021
CausalAdv: Adversarial Robustness through the Lens of Causality Yonggang Zhang Biwei Huang Tongliang Liu Gang Niu Xinmei Tian Bo Han Bernhard Schölkopf Kun Zhang OOD AAML CML 82 36 0 11 Jun 2021
Adversarial purification with Score-based generative models Jongmin Yoon Sung Ju Hwang Juho Lee DiffM 92 160 0 11 Jun 2021
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix Maximilian Lam Gu-Yeon Wei David Brooks Vijay Janapa Reddi Michael Mitzenmacher FedML 104 65 0 10 Jun 2021
An Ensemble Approach Towards Adversarial Robustness Haifeng Qian AAML UQCV 24 0 0 10 Jun 2021
Deep neural network loses attention to adversarial images Shashank Kotyan Danilo Vasconcellos Vargas AAML GAN 45 4 0 10 Jun 2021
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training Dawei Zhou N. Wang Xinbo Gao Bo Han Jun Yu Xiaoyu Wang Tongliang Liu AAML 71 4 0 10 Jun 2021
Towards Defending against Adversarial Examples via Attack-Invariant Features Dawei Zhou Tongliang Liu Bo Han N. Wang Chunlei Peng Xinbo Gao AAML 82 45 0 09 Jun 2021
Attacking Adversarial Attacks as A Defense Boxi Wu Heng Pan Li Shen Jindong Gu Shuai Zhao Zhifeng Li Deng Cai Xiaofei He Wei Liu AAML 93 33 0 09 Jun 2021
Taxonomy of Machine Learning Safety: A Survey and Primer Sina Mohseni Haotao Wang Zhiding Yu Chaowei Xiao Zhangyang Wang J. Yadawa 95 32 0 09 Jun 2021
Provably Robust Detection of Out-of-distribution Data (almost) for free Alexander Meinke Julian Bitterwolf Matthias Hein OODD 70 22 0 08 Jun 2021
Reveal of Vision Transformers Robustness against Adversarial Attacks Ahmed Aldahdooh W. Hamidouche Olivier Déforges ViT 55 60 0 07 Jun 2021