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DeepFool: a simple and accurate method to fool deep neural networks

14 November 2015

Seyed-Mohsen Moosavi-Dezfooli

Papers citing "DeepFool: a simple and accurate method to fool deep neural networks"

50 / 2,298 papers shown

Title
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases Pierre Stock Moustapha Cissé FaML 94 46 0 30 Nov 2017
On the Robustness of Semantic Segmentation Models to Adversarial Attacks Anurag Arnab O. Mikšík Philip Torr AAML 115 308 0 27 Nov 2017
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation Y. Yoo Seonguk Park Junyoung Choi Sangdoo Yun Nojun Kwak AAML 50 4 0 27 Nov 2017
Geometric robustness of deep networks: analysis and improvement Can Kanbak Seyed-Mohsen Moosavi-Dezfooli P. Frossard OOD AAML 119 131 0 24 Nov 2017
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning Ambrish Rawat Martin Wistuba Maria-Irina Nicolae BDL AAML 64 39 0 22 Nov 2017
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training Xi Wu Uyeong Jang Jiefeng Chen Lingjiao Chen S. Jha AAML 94 21 0 21 Nov 2017
Adversarial Attacks Beyond the Image Space Fangyin Wei Chenxi Liu Yu-Siang Wang Weichao Qiu Lingxi Xie Yu-Wing Tai Chi-Keung Tang Alan Yuille AAML 126 150 0 20 Nov 2017
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models Kathrin Grosse David Pfaff M. Smith Michael Backes AAML 82 9 0 17 Nov 2017
Enhanced Attacks on Defensively Distilled Deep Neural Networks Yujia Liu Weiming Zhang Shaohua Li Nenghai Yu AAML 76 6 0 16 Nov 2017
Defense against Universal Adversarial Perturbations Naveed Akhtar Jian Liu Ajmal Mian AAML 103 208 0 16 Nov 2017
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 164 1,068 0 06 Nov 2017
HyperNetworks with statistical filtering for defending adversarial examples Zhun Sun Mete Ozay Takayuki Okatani AAML 54 16 0 06 Nov 2017
Attacking Binarized Neural Networks A. Galloway Graham W. Taylor M. Moussa MQ AAML 81 106 0 01 Nov 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 149 1,409 0 31 Oct 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 141 791 0 30 Oct 2017
A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition Yueru Chen Zhuwei Xu Shanshan Cai Yujian Lang C.-C. Jay Kuo 66 35 0 29 Oct 2017
Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha Hongseok Namkoong Riccardo Volpi John C. Duchi OOD 145 866 0 29 Oct 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 153 874 0 29 Oct 2017
One pixel attack for fooling deep neural networks Jiawei Su Danilo Vasconcellos Vargas Kouichi Sakurai AAML 220 2,331 0 24 Oct 2017
On Data-Driven Saak Transform C.-C. Jay Kuo Yueru Chen AI4TS 87 94 0 11 Oct 2017
Standard detectors aren't (currently) fooled by physical adversarial stop signs Jiajun Lu Hussein Sibai Evan Fabry David A. Forsyth AAML 101 59 0 09 Oct 2017
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight Yen-Chen Lin Ming-Yuan Liu Min Sun Jia-Bin Huang AAML 96 48 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 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
Verifying Properties of Binarized Deep Neural Networks Nina Narodytska S. Kasiviswanathan L. Ryzhyk Shmuel Sagiv T. Walsh AAML 117 217 0 19 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
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
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
CNN Fixations: An unraveling approach to visualize the discriminative image regions Konda Reddy Mopuri Utsav Garg R. Venkatesh Babu AAML 91 56 0 22 Aug 2017
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Yinpeng Dong Hang Su Jun Zhu Fan Bao AAML 143 129 0 18 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
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
Confidence estimation in Deep Neural networks via density modelling Akshayvarun Subramanya Suraj Srinivas R. Venkatesh Babu 65 51 0 21 Jul 2017
Efficient Defenses Against Adversarial Attacks Valentina Zantedeschi Maria-Irina Nicolae Ambrish Rawat AAML 74 297 0 21 Jul 2017
Fast Feature Fool: A data independent approach to universal adversarial perturbations Konda Reddy Mopuri Utsav Garg R. Venkatesh Babu AAML 126 205 0 18 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
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 82 283 0 13 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
A Survey on Resilient Machine Learning Atul Kumar S. Mehta OOD AAML 83 16 0 11 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
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 341 12,169 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 114 242 0 15 Jun 2017