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Adversarial examples in the physical world

8 July 2016

Papers citing "Adversarial examples in the physical world"

50 / 2,769 papers shown

Title
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks D. Gopinath Guy Katz C. Păsăreanu Clark W. Barrett AAML 141 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
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification Xiaoyu Cao Neil Zhenqiang Gong AAML 85 212 0 17 Sep 2017
A Learning and Masking Approach to Secure Learning Linh Nguyen Sky Wang Arunesh Sinha AAML 63 2 0 13 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 80 641 0 13 Sep 2017
Can Deep Neural Networks Match the Related Objects?: A Survey on ImageNet-trained Classification Models Han S. Lee Heechul Jung Alex A. Agarwal Junmo Kim 85 6 0 12 Sep 2017
Art of singular vectors and universal adversarial perturbations Valentin Khrulkov Ivan Oseledets AAML 78 132 0 11 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 107 118 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
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid Marco Melis Ambra Demontis Battista Biggio Gavin Brown Giorgio Fumera Fabio Roli AAML 79 98 0 23 Aug 2017
Learning Universal Adversarial Perturbations with Generative Models Jamie Hayes G. Danezis AAML 84 54 0 17 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 115 1,894 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
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 143 595 0 27 Jul 2017
Synthesizing Robust Adversarial Examples Anish Athalye Logan Engstrom Ilya Sutskever Kevin Kwok AAML 68 66 0 24 Jul 2017
Efficient Defenses Against Adversarial Attacks Valentina Zantedeschi Maria-Irina Nicolae Ambrish Rawat AAML 74 297 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
Houdini: Fooling Deep Structured Prediction Models Moustapha Cissé Yossi Adi Natalia Neverova Joseph Keshet AAML 90 272 0 17 Jul 2017
Adversarial Dropout for Supervised and Semi-supervised Learning Sungrae Park Jun-Keon Park Su-Jin Shin Il-Chul Moon GAN 99 174 0 12 Jul 2017
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 104 282 0 12 Jul 2017
Towards Crafting Text Adversarial Samples Suranjana Samanta S. Mehta AAML 85 222 0 10 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
UPSET and ANGRI : Breaking High Performance Image Classifiers Sayantan Sarkar Ankan Bansal U. Mahbub Rama Chellappa AAML 83 108 0 04 Jul 2017
A Closer Look at Memorization in Deep Networks Devansh Arpit Stanislaw Jastrzebski Nicolas Ballas David M. Krueger Emmanuel Bengio ... Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio Simon Lacoste-Julien TDI 174 1,832 0 16 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini Basel Alomair AAML 117 242 0 15 Jun 2017
Certified Defenses for Data Poisoning Attacks Jacob Steinhardt Pang Wei Koh Percy Liang AAML 168 762 0 09 Jun 2017
Towards Robust Detection of Adversarial Examples Tianyu Pang Chao Du Yinpeng Dong Jun Zhu AAML 87 18 0 02 Jun 2017
MagNet: a Two-Pronged Defense against Adversarial Examples Dongyu Meng Hao Chen AAML 56 1,210 0 25 May 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein Maksym Andriushchenko AAML 131 512 0 23 May 2017
Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction Bin Liang Hongcheng Li Miaoqiang Su Xirong Li Wenchang Shi Xiaofeng Wang AAML 133 219 0 23 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 140 1,869 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 217 2,738 0 19 May 2017
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Li-Wei Chen Michael E. Kounavis Duen Horng Chau AAML 89 307 0 08 May 2017
Detecting Adversarial Samples Using Density Ratio Estimates Lovedeep Gondara AAML 31 4 0 05 May 2017
Maximum Resilience of Artificial Neural Networks Chih-Hong Cheng Georg Nührenberg Harald Ruess AAML 145 284 0 28 Apr 2017
Universal Adversarial Perturbations Against Semantic Image Segmentation J. H. Metzen Mummadi Chaithanya Kumar Thomas Brox Volker Fischer AAML 177 288 0 19 Apr 2017
Adversarial and Clean Data Are Not Twins Zhitao Gong Wenlu Wang Wei-Shinn Ku AAML 64 158 0 17 Apr 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 127 558 0 11 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 123 1,528 0 11 Apr 2017
Enhancing Robustness of Machine Learning Systems via Data Transformations A. Bhagoji Daniel Cullina Chawin Sitawarin Prateek Mittal AAML 114 231 0 09 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 104 1,283 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 Image Perturbation for Privacy Protection -- A Game Theory Perspective Seong Joon Oh Mario Fritz Bernt Schiele CVBM AAML 431 162 0 28 Mar 2017
Adversarial Transformation Networks: Learning to Generate Adversarial Examples S. Baluja Ian S. Fischer GAN 87 286 0 28 Mar 2017
Adversarial Examples for Semantic Segmentation and Object Detection Cihang Xie Jianyu Wang Zhishuai Zhang Yuyin Zhou Lingxi Xie Alan Yuille GAN AAML 113 935 0 24 Mar 2017
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems Hossein Hosseini Yize Chen Sreeram Kannan Baosen Zhang Radha Poovendran AAML 90 107 0 13 Mar 2017
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents Yen-Chen Lin Zhang-Wei Hong Yuan-Hong Liao Meng-Li Shih Ming-Yuan Liu Min Sun AAML 141 418 0 08 Mar 2017
Adversarial Examples for Semantic Image Segmentation Volker Fischer Mummadi Chaithanya Kumar J. H. Metzen Thomas Brox SSeg GAN AAML 102 119 0 03 Mar 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 117 896 0 01 Mar 2017