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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"

50 / 1,929 papers shown

Title
Towards Adversarially Robust Object Detection Haichao Zhang Jianyu Wang AAML ObjD 139 131 0 24 Jul 2019
Enhancing Adversarial Example Transferability with an Intermediate Level Attack Qian Huang Isay Katsman Horace He Zeqi Gu Serge J. Belongie Ser-Nam Lim SILM AAML 106 248 0 23 Jul 2019
Structure-Invariant Testing for Machine Translation Pinjia He Clara Meister Z. Su 75 106 0 19 Jul 2019
Connecting Lyapunov Control Theory to Adversarial Attacks Arash Rahnama A. Nguyen Edward Raff AAML 29 6 0 17 Jul 2019
Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods Arif Siddiqi AAML 64 11 0 17 Jul 2019
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving Yulong Cao Chaowei Xiao Benjamin Cyr Yimeng Zhou Wonseok Park Sara Rampazzi Qi Alfred Chen Kevin Fu Z. Morley Mao AAML 63 544 0 16 Jul 2019
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning Bao Wang Stanley J. Osher AAML AI4CE 77 10 0 16 Jul 2019
Stateful Detection of Black-Box Adversarial Attacks Steven Chen Nicholas Carlini D. Wagner AAML MLAU 69 126 0 12 Jul 2019
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences Mathias Kraus Stefan Feuerriegel 64 110 0 11 Jul 2019
Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn Ziv Katzir Yuval Elovici AAML 20 3 0 11 Jul 2019
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions Yao Qin Nicholas Frosst S. Sabour Colin Raffel G. Cottrell Geoffrey E. Hinton GAN AAML 83 72 0 05 Jul 2019
Adversarial Robustness through Local Linearization Chongli Qin James Martens Sven Gowal Dilip Krishnan Krishnamurthy Dvijotham Alhussein Fawzi Soham De Robert Stanforth Pushmeet Kohli AAML 94 308 0 04 Jul 2019
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Francesco Croce Matthias Hein AAML 149 493 0 03 Jul 2019
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation Nader Asadi Amirm. Sarfi Mehrdad Hosseinzadeh Sahba Tahsini M. Eftekhari AAML 32 2 0 01 Jul 2019
Accurate, reliable and fast robustness evaluation Wieland Brendel Jonas Rauber Matthias Kümmerer Ivan Ustyuzhaninov Matthias Bethge AAML OOD 97 113 0 01 Jul 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks Mantas Mazeika Saurav Kadavath Basel Alomair OOD SSL 69 953 0 28 Jun 2019
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense Guanxiong Liu Issa M. Khalil Abdallah Khreishah AAML 37 2 0 27 Jun 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 81 36 0 27 Jun 2019
Defending Adversarial Attacks by Correcting logits Yifeng Li Lingxi Xie Ya Zhang Rui Zhang Yanfeng Wang Qi Tian AAML 41 5 0 26 Jun 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 89 105 0 25 Jun 2019
Defending Against Adversarial Examples with K-Nearest Neighbor Chawin Sitawarin David Wagner AAML 91 29 0 23 Jun 2019
Improving the robustness of ImageNet classifiers using elements of human visual cognition A. Orhan Brenden M. Lake VLM 60 5 0 20 Jun 2019
Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao Tianle Cai Haochuan Li Liwei Wang Cho-Jui Hsieh Jason D. Lee AAML 113 109 0 19 Jun 2019
Improving Black-box Adversarial Attacks with a Transfer-based Prior Shuyu Cheng Yinpeng Dong Tianyu Pang Hang Su Jun Zhu AAML 94 274 0 17 Jun 2019
MixUp as Directional Adversarial Training Guillaume P. Archambault Yongyi Mao Hongyu Guo Richong Zhang AAML 58 23 0 17 Jun 2019
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy Alex Lamb Vikas Verma Kenji Kawaguchi Alexander Matyasko Savya Khosla Arno Solin Yoshua Bengio AAML 74 99 0 16 Jun 2019
Defending Against Adversarial Attacks Using Random Forests Yifan Ding Liqiang Wang Huan Zhang Jinfeng Yi Deliang Fan Boqing Gong AAML 64 14 0 16 Jun 2019
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences Shashank Kotyan Danilo Vasconcellos Vargas Moe Matsuki 39 0 0 15 Jun 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 109 351 0 14 Jun 2019
Towards Compact and Robust Deep Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 82 40 0 14 Jun 2019
$Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary$ Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary Shashank Kotyan Danilo Vasconcellos Vargas AAML 34 8 0 14 Jun 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 114 336 0 13 Jun 2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks R. Sahay Rehana Mahfuz Aly El Gamal AAML 33 5 0 13 Jun 2019
E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles M. Kettunen Erik Härkönen J. Lehtinen AAML 65 63 0 10 Jun 2019
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective Lu Wang Xuanqing Liu Jinfeng Yi Zhi Zhou Cho-Jui Hsieh AAML 83 22 0 10 Jun 2019
Robustness Verification of Tree-based Models Hongge Chen Huan Zhang Si Si Yang Li Duane S. Boning Cho-Jui Hsieh AAML 103 77 0 10 Jun 2019
Improving Neural Language Modeling via Adversarial Training Dilin Wang Chengyue Gong Qiang Liu AAML 122 119 0 10 Jun 2019
Improved Adversarial Robustness via Logit Regularization Methods Cecilia Summers M. Dinneen AAML 59 7 0 10 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 134 552 0 09 Jun 2019
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad Yangqiu Song AAML 83 37 0 09 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks Maksym Andriushchenko Matthias Hein 84 62 0 08 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 93 101 0 08 Jun 2019
Making targeted black-box evasion attacks effective and efficient Mika Juuti B. Atli Nadarajah Asokan AAML MIACV MLAU 49 8 0 08 Jun 2019
Robustness for Non-Parametric Classification: A Generic Attack and Defense Yao-Yuan Yang Cyrus Rashtchian Yizhen Wang Kamalika Chaudhuri SILM AAML 92 43 0 07 Jun 2019
A cryptographic approach to black box adversarial machine learning Kevin Shi Daniel J. Hsu Allison Bishop AAML 18 3 0 07 Jun 2019
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data Yan Li Ethan X. Fang Huan Xu T. Zhao 92 16 0 07 Jun 2019
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness Walt Woods Jack H Chen C. Teuscher AAML 66 46 0 07 Jun 2019
Should Adversarial Attacks Use Pixel p-Norm? Ayon Sen Xiaojin Zhu Liam Marshall Robert D. Nowak 51 21 0 06 Jun 2019
MNIST-C: A Robustness Benchmark for Computer Vision Norman Mu Justin Gilmer 75 214 0 05 Jun 2019
Enhancing Gradient-based Attacks with Symbolic Intervals Shiqi Wang Yizheng Chen Ahmed Abdou Suman Jana AAML 58 15 0 05 Jun 2019