Parseval Networks: Improving Robustness to Adversarial Examples

28 April 2017

Papers citing "Parseval Networks: Improving Robustness to Adversarial Examples"

37 / 487 papers shown

Title
Predicting Adversarial Examples with High Confidence A. Galloway Graham W. Taylor M. Moussa AAML 18 9 0 13 Feb 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 33 296 0 12 Feb 2018
Certified Robustness to Adversarial Examples with Differential Privacy Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel J. Hsu Suman Jana SILM AAML 27 924 0 09 Feb 2018
Fibres of Failure: Classifying errors in predictive processes L. Carlsson Gunnar Carlsson Mikael Vejdemo-Johansson AI4CE 24 4 0 09 Feb 2018
First-order Adversarial Vulnerability of Neural Networks and Input Dimension Carl-Johann Simon-Gabriel Yann Ollivier Léon Bottou Bernhard Schölkopf David Lopez-Paz AAML 22 48 0 05 Feb 2018
Certified Defenses against Adversarial Examples Aditi Raghunathan Jacob Steinhardt Percy Liang AAML 16 965 0 29 Jan 2018
Fooling End-to-end Speaker Verification by Adversarial Examples Felix Kreuk Yossi Adi Moustapha Cissé Joseph Keshet AAML 11 202 0 10 Jan 2018
Adversarial Spheres Justin Gilmer Luke Metz Fartash Faghri S. Schoenholz M. Raghu Martin Wattenberg Ian Goodfellow AAML 17 7 0 09 Jan 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Saeed Mian AAML 22 1,854 0 02 Jan 2018
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 23 174 0 26 Dec 2017
Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems Kexin Pei Linjie Zhu Yinzhi Cao Junfeng Yang Carl Vondrick Suman Jana AAML 19 102 0 05 Dec 2017
Improving Network Robustness against Adversarial Attacks with Compact Convolution Rajeev Ranjan S. Sankaranarayanan Carlos D. Castillo Rama Chellappa AAML 19 14 0 03 Dec 2017
Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo Yoshua Bengio AAML 22 249 0 30 Nov 2017
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases Pierre Stock Moustapha Cissé FaML 23 46 0 30 Nov 2017
On the Robustness of Semantic Segmentation Models to Adversarial Attacks Anurag Arnab O. Mikšík Philip H. S. Torr AAML 23 304 0 27 Nov 2017
Intriguing Properties of Adversarial Examples E. D. Cubuk Barret Zoph S. Schoenholz Quoc V. Le AAML 23 84 0 08 Nov 2017
Provable defenses against adversarial examples via the convex outer adversarial polytope Eric Wong J. Zico Kolter AAML 34 1,487 0 02 Nov 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé L. V. D. van der Maaten AAML 28 1,386 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 26 785 0 30 Oct 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Y. Zou FAtt AAML 22 856 0 29 Oct 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 22 9,583 0 25 Oct 2017
Word Translation Without Parallel Data Alexis Conneau Guillaume Lample MarcÁurelio Ranzato Ludovic Denoyer Hervé Jégou 169 1,635 0 11 Oct 2017
Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks Lei Huang Xianglong Liu B. Lang Adams Wei Yu Yongliang Wang Bo Li ODL 19 223 0 16 Sep 2017
Art of singular vectors and universal adversarial perturbations Valentin Khrulkov Ivan V. Oseledets AAML 17 132 0 11 Sep 2017
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars Yuchi Tian Kexin Pei Suman Jana Baishakhi Ray AAML 9 1,347 0 28 Aug 2017
Houdini: Fooling Deep Structured Prediction Models Moustapha Cissé Yossi Adi Natalia Neverova Joseph Keshet AAML 22 268 0 17 Jul 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 13 1,199 0 26 Jun 2017
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations A. Bietti Julien Mairal 14 7 0 09 Jun 2017
Kronecker Recurrent Units C. Jose Moustapha Cissé F. Fleuret ODL 24 45 0 29 May 2017
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks Chang Song Hsin-Pai Cheng Huanrui Yang Sicheng Li Chunpeng Wu Qing Wu H. Li Yiran Chen AAML 13 2 0 27 May 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein Maksym Andriushchenko AAML 29 505 0 23 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick D. McDaniel AAML 10 2,697 0 19 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 17 1,350 0 18 May 2017
Optimization on Product Submanifolds of Convolution Kernels Mete Ozay Takayuki Okatani AAML 18 0 0 22 Jan 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction Thomas Wiatowski Helmut Bölcskei FAtt 18 361 0 19 Dec 2015
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization Uri Shaham Yutaro Yamada S. Negahban AAML 14 73 0 17 Nov 2015