Towards Deep Learning Models Resistant to Adversarial Attacks

19 June 2017

Papers citing "Towards Deep Learning Models Resistant to Adversarial Attacks"

50 / 6,519 papers shown

Title
Fooling a Real Car with Adversarial Traffic Signs N. Morgulis Alexander Kreines Shachar Mendelowitz Yuval Weisglass AAML 16 91 0 30 Jun 2019
Training individually fair ML models with Sensitive Subspace Robustness Mikhail Yurochkin Amanda Bower Yuekai Sun FaML OOD 27 119 0 28 Jun 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks Mantas Mazeika Saurav Kadavath D. Song OOD SSL 10 936 0 28 Jun 2019
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense Guanxiong Liu Issa M. Khalil Abdallah Khreishah AAML 20 2 0 27 Jun 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 24 36 0 27 Jun 2019
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness Fanny Yang Zuowen Wang C. Heinze-Deml 28 42 0 26 Jun 2019
Defending Adversarial Attacks by Correcting logits Yifeng Li Lingxi Xie Ya Zhang Rui Zhang Yanfeng Wang Qi Tian AAML 29 5 0 26 Jun 2019
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks Tribhuvanesh Orekondy Bernt Schiele Mario Fritz AAML 19 164 0 26 Jun 2019
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection Kang Liu Haoyu Yang Yuzhe Ma Benjamin Tan Bei Yu Evangeline F. Y. Young Ramesh Karri S. Garg AAML 20 10 0 25 Jun 2019
Defending Against Adversarial Examples with K-Nearest Neighbor Chawin Sitawarin David Wagner AAML 11 29 0 23 Jun 2019
A Fourier Perspective on Model Robustness in Computer Vision Dong Yin Raphael Gontijo-Lopes Jonathon Shlens E. D. Cubuk Justin Gilmer OOD 37 490 0 21 Jun 2019
On Physical Adversarial Patches for Object Detection Mark Lee Zico Kolter AAML 19 166 0 20 Jun 2019
Improving the robustness of ImageNet classifiers using elements of human visual cognition A. Orhan Brenden M. Lake VLM 11 5 0 20 Jun 2019
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield Dou Goodman Tao Wei AAML 27 6 0 19 Jun 2019
A unified view on differential privacy and robustness to adversarial examples Rafael Pinot Florian Yger Cédric Gouy-Pailler Jamal Atif AAML 21 17 0 19 Jun 2019
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing Haonan Qiu Chaowei Xiao Lei Yang Xinchen Yan Honglak Lee Yue Liu AAML 28 170 0 19 Jun 2019
Global Adversarial Attacks for Assessing Deep Learning Robustness Hanbin Hu Mitt Shah Jianhua Z. Huang Peng Li AAML 28 4 0 19 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 21 107 0 19 Jun 2019
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks F. Assion Peter Schlicht Florens Greßner W. Günther Fabian Hüger Nico M. Schmidt Umair Rasheed AAML 25 14 0 17 Jun 2019
Improving Black-box Adversarial Attacks with a Transfer-based Prior Shuyu Cheng Yinpeng Dong Tianyu Pang Hang Su Jun Zhu AAML 34 271 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 30 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 21 14 0 16 Jun 2019
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences Shashank Kotyan Danilo Vasconcellos Vargas Moe Matsuki 12 0 0 15 Jun 2019
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks Felipe A. Mejia Paul Gamble Z. Hampel-Arias M. Lomnitz Nina Lopatina Lucas Tindall M. Barrios SILM 27 18 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 17 344 0 14 Jun 2019
Distributionally Robust Counterfactual Risk Minimization Louis Faury Ugo Tanielian Flavian Vasile E. Smirnova Elvis Dohmatob 28 45 0 14 Jun 2019
Towards Compact and Robust Deep Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 35 40 0 14 Jun 2019
Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks T. Brunner Frederik Diehl Alois Knoll AAML 14 8 0 14 Jun 2019
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 24 240 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 17 8 0 14 Jun 2019
Lower Bounds for Adversarially Robust PAC Learning Dimitrios I. Diochnos Saeed Mahloujifar Mohammad Mahmoody AAML 27 26 0 13 Jun 2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks R. Sahay Rehana Mahfuz Aly El Gamal AAML 22 5 0 13 Jun 2019
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers Guang-He Lee Yang Yuan Shiyu Chang Tommi Jaakkola AAML 25 122 0 12 Jun 2019
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks Mahyar Fazlyab Alexander Robey Hamed Hassani M. Morari George J. Pappas 39 450 0 12 Jun 2019
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks Ziang Yan Yiwen Guo Changshui Zhang AAML 36 110 0 11 Jun 2019
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong Xue Lin AAML 13 449 0 10 Jun 2019
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective Lu Wang Xuanqing Liu Jinfeng Yi Zhi-Hua Zhou Cho-Jui Hsieh AAML 39 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 22 76 0 10 Jun 2019
Intriguing properties of adversarial training at scale Cihang Xie Alan Yuille AAML 13 68 0 10 Jun 2019
Improved Adversarial Robustness via Logit Regularization Methods Cecilia Summers M. Dinneen AAML 42 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 45 538 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 30 35 0 09 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks Maksym Andriushchenko Matthias Hein 28 61 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 22 101 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 34 42 0 07 Jun 2019
A cryptographic approach to black box adversarial machine learning Kevin Shi Daniel J. Hsu Allison Bishop AAML 16 3 0 07 Jun 2019
Kernelized Capsule Networks Taylor W. Killian Justin A. Goodwin Olivia M. Brown Sung-Hyun Son GAN 36 2 0 07 Jun 2019
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data Yan Li Ethan X. Fang Huan Xu T. Zhao 17 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 18 46 0 07 Jun 2019
Robust Attacks against Multiple Classifiers Juan C. Perdomo Yaron Singer AAML 18 10 0 06 Jun 2019