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Towards Evaluating the Robustness of Neural Networks

16 August 2016

Papers citing "Towards Evaluating the Robustness of Neural Networks"

50 / 4,015 papers shown

Title
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 147 791 0 30 Oct 2017
Attacking the Madry Defense Model with $L_1$ -based Adversarial Examples Yash Sharma Pin-Yu Chen 126 118 0 30 Oct 2017
Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha Hongseok Namkoong Riccardo Volpi John C. Duchi OOD 149 866 0 29 Oct 2017
One pixel attack for fooling deep neural networks Jiawei Su Danilo Vasconcellos Vargas Kouichi Sakurai AAML 220 2,336 0 24 Oct 2017
Feature-Guided Black-Box Safety Testing of Deep Neural Networks Matthew Wicker Xiaowei Huang Marta Kwiatkowska AAML 83 236 0 21 Oct 2017
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight Yen-Chen Lin Ming-Yuan Liu Min Sun Jia-Bin Huang AAML 104 49 0 02 Oct 2017
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks D. Gopinath Guy Katz C. Păsăreanu Clark W. Barrett AAML 144 87 0 02 Oct 2017
Provably Minimally-Distorted Adversarial Examples Nicholas Carlini Guy Katz Clark W. Barrett D. Dill AAML 105 89 0 29 Sep 2017
Fooling Vision and Language Models Despite Localization and Attention Mechanism Xiaojun Xu Xinyun Chen Chang-rui Liu Anna Rohrbach Trevor Darrell Basel Alomair AAML 106 41 0 25 Sep 2017
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification Xiaoyu Cao Neil Zhenqiang Gong AAML 85 212 0 17 Sep 2017
Robustness Analysis of Visual QA Models by Basic Questions Jia-Hong Huang Cuong Duc Dao Modar Alfadly C. Huck Yang Guohao Li OOD 65 24 0 14 Sep 2017
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples Pin-Yu Chen Yash Sharma Huan Zhang Jinfeng Yi Cho-Jui Hsieh AAML 86 641 0 13 Sep 2017
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks Thilo Strauss Markus Hanselmann Andrej Junginger Holger Ulmer AAML 93 137 0 11 Sep 2017
Towards Proving the Adversarial Robustness of Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel J. Kochenderfer AAML OOD 112 118 0 08 Sep 2017
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning B. Rouhani Mohammad Samragh Mojan Javaheripi T. Javidi F. Koushanfar AAML 53 15 0 08 Sep 2017
Practical Attacks Against Graph-based Clustering Yizheng Chen Yacin Nadji Athanasios Kountouras Fabian Monrose R. Perdisci M. Antonakakis N. Vasiloglou AAML 63 87 0 29 Aug 2017
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars Yuchi Tian Kexin Pei Suman Jana Baishakhi Ray AAML 99 1,365 0 28 Aug 2017
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features Liang Tong Yue Liu Chen Hajaj Chaowei Xiao Ning Zhang Yevgeniy Vorobeychik AAML OOD 52 88 0 28 Aug 2017
Modular Learning Component Attacks: Today's Reality, Tomorrow's Challenge Xinyang Zhang Yujie Ji Ting Wang AAML 39 2 0 25 Aug 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 127 1,896 0 14 Aug 2017
Cascade Adversarial Machine Learning Regularized with a Unified Embedding Taesik Na J. Ko Saibal Mukhopadhyay AAML GAN 95 102 0 08 Aug 2017
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning Andrew P. Norton Yanjun Qi AAML 75 47 0 01 Aug 2017
Robust Physical-World Attacks on Deep Learning Models Kevin Eykholt Ivan Evtimov Earlence Fernandes Yue Liu Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno Basel Alomair AAML 145 595 0 27 Jul 2017
Efficient Defenses Against Adversarial Attacks Valentina Zantedeschi Maria-Irina Nicolae Ambrish Rawat AAML 76 297 0 21 Jul 2017
APE-GAN: Adversarial Perturbation Elimination with GAN Shiwei Shen Guoqing Jin Feng Dai Yongdong Zhang GAN 122 221 0 18 Jul 2017
A Formal Framework to Characterize Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 47 19 0 12 Jul 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 123 172 0 08 Jul 2017
Efficient Data Representation by Selecting Prototypes with Importance Weights Karthik S. Gurumoorthy Amit Dhurandhar Guillermo Cecchi Charu Aggarwal 122 22 0 05 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 508 12,186 0 19 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini Basel Alomair AAML 122 241 0 15 Jun 2017
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples Yizhen Wang S. Jha Kamalika Chaudhuri AAML 251 155 0 13 Jun 2017
TIP: Typifying the Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 95 36 0 09 Jun 2017
Adversarial-Playground: A Visualization Suite for Adversarial Sample Generation Andrew P. Norton Yanjun Qi AAML 26 0 0 06 Jun 2017
Towards Robust Detection of Adversarial Examples Tianyu Pang Chao Du Yinpeng Dong Jun Zhu AAML 87 18 0 02 Jun 2017
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples Weilin Xu David Evans Yanjun Qi AAML 68 42 0 30 May 2017
MagNet: a Two-Pronged Defense against Adversarial Examples Dongyu Meng Hao Chen AAML 56 1,211 0 25 May 2017
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction Bin Liang Hongcheng Li Miaoqiang Su Xirong Li Wenchang Shi Wenyuan Xu AAML 139 220 0 23 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 142 1,870 0 20 May 2017
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense Sailik Sengupta Tathagata Chakraborti S. Kambhampati AAML 139 63 0 19 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 217 2,739 0 19 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 142 1,376 0 18 May 2017
Extending Defensive Distillation Nicolas Papernot Patrick McDaniel AAML 91 119 0 15 May 2017
DeepCorrect: Correcting DNN models against Image Distortions Tejas S. Borkar Lina Karam 133 93 0 05 May 2017
Deep Text Classification Can be Fooled Bin Liang Hongcheng Li Miaoqiang Su Pan Bian Xirong Li Wenchang Shi AAML 85 427 0 26 Apr 2017
Universal Adversarial Perturbations Against Semantic Image Segmentation J. H. Metzen Mummadi Chaithanya Kumar Thomas Brox Volker Fischer AAML 181 289 0 19 Apr 2017
Google's Cloud Vision API Is Not Robust To Noise Hossein Hosseini Baicen Xiao Radha Poovendran AAML 77 124 0 16 Apr 2017
Enhancing Robustness of Machine Learning Systems via Data Transformations A. Bhagoji Daniel Cullina Chawin Sitawarin Prateek Mittal AAML 134 231 0 09 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 106 1,288 0 04 Apr 2017
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly Jiajun Lu Theerasit Issaranon David A. Forsyth GAN 120 381 0 01 Apr 2017
Adversarial Transformation Networks: Learning to Generate Adversarial Examples S. Baluja Ian S. Fischer GAN 87 286 0 28 Mar 2017