Towards Deep Neural Network Architectures Robust to Adversarial Examples

11 December 2014

Papers citing "Towards Deep Neural Network Architectures Robust to Adversarial Examples"

50 / 162 papers shown

Title
Fast Geometrically-Perturbed Adversarial Faces Ali Dabouei Sobhan Soleymani J. Dawson Nasser M. Nasrabadi CVBM AAML 29 65 0 24 Sep 2018
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples Deqiang Li Ramesh Baral Tao Li Han Wang Qianmu Li Shouhuai Xu AAML 25 21 0 18 Sep 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
Adversarial Examples in Deep Learning: Characterization and Divergence Wenqi Wei Ling Liu Margaret Loper Stacey Truex Lei Yu Mehmet Emre Gursoy Yanzhao Wu AAML SILM 33 18 0 29 Jun 2018
Detection based Defense against Adversarial Examples from the Steganalysis Point of View Jiayang Liu Weiming Zhang Yiwei Zhang Dongdong Hou Yujia Liu Hongyue Zha Nenghai Yu AAML 22 98 0 21 Jun 2018
Gradient Adversarial Training of Neural Networks Ayan Sinha Zhao Chen Vijay Badrinarayanan Andrew Rabinovich AAML 30 33 0 21 Jun 2018
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks Jan Svoboda Jonathan Masci Federico Monti M. Bronstein Leonidas J. Guibas AAML GNN 33 41 0 31 May 2018
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders Partha Ghosh Arpan Losalka Michael J. Black AAML 21 77 0 31 May 2018
Defending Against Adversarial Attacks by Leveraging an Entire GAN G. Santhanam Paulina Grnarova AAML 16 40 0 27 May 2018
Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness Carlos Lassance Vincent Gripon Antonio Ortega AAML 24 16 0 24 May 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 14 369 0 23 May 2018
Bidirectional Learning for Robust Neural Networks S. Pontes-Filho Marcus Liwicki 13 9 0 21 May 2018
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing Jingyi Wang Jun Sun Peixin Zhang Xinyu Wang AAML 21 41 0 14 May 2018
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations Alex Lamb Jonathan Binas Anirudh Goyal Dmitriy Serdyuk Sandeep Subramanian Ioannis Mitliagkas Yoshua Bengio OOD 34 43 0 07 Apr 2018
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples Ayse Elvan Aydemir A. Temi̇zel T. Taşkaya-Temizel AAML 8 30 0 28 Mar 2018
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training Derui Wang Chaoran Li S. Wen Surya Nepal Yang Xiang AAML 41 29 0 14 Mar 2018
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples Jake Zhao Kyunghyun Cho AAML 24 20 0 26 Feb 2018
Deep Defense: Training DNNs with Improved Adversarial Robustness Ziang Yan Yiwen Guo Changshui Zhang AAML 38 109 0 23 Feb 2018
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 43 225 0 19 Feb 2018
Are Generative Classifiers More Robust to Adversarial Attacks? Yingzhen Li John Bradshaw Yash Sharma AAML 57 78 0 19 Feb 2018
Security and Privacy Approaches in Mixed Reality: A Literature Survey Jaybie A. de Guzman Kanchana Thilakarathna Aruna Seneviratne 26 132 0 15 Feb 2018
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks Qi Liu Tao Liu Zihao Liu Yanzhi Wang Yier Jin Wujie Wen AAML 27 48 0 14 Feb 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Bo-wen Li Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck D. Song Michael E. Houle James Bailey AAML 37 728 0 08 Jan 2018
Gradient Regularization Improves Accuracy of Discriminative Models D. Varga Adrián Csiszárik Zsolt Zombori 18 53 0 28 Dec 2017
The Robust Manifold Defense: Adversarial Training using Generative Models A. Jalal Andrew Ilyas C. Daskalakis A. Dimakis AAML 31 174 0 26 Dec 2017
Improving Network Robustness against Adversarial Attacks with Compact Convolution Rajeev Ranjan S. Sankaranarayanan Carlos D. Castillo Rama Chellappa AAML 21 14 0 03 Dec 2017
Measuring the tendency of CNNs to Learn Surface Statistical Regularities Jason Jo Yoshua Bengio AAML 26 249 0 30 Nov 2017
Evaluating Robustness of Neural Networks with Mixed Integer Programming Vincent Tjeng Kai Y. Xiao Russ Tedrake AAML 52 117 0 20 Nov 2017
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks Thilo Strauss Markus Hanselmann Andrej Junginger Holger Ulmer AAML 20 134 0 11 Sep 2017
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning B. Rouhani Mohammad Samragh Mojan Javaheripi T. Javidi F. Koushanfar AAML 12 15 0 08 Sep 2017
Towards Deep Learning Models Resistant to Adversarial Attacks A. Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 83 11,872 0 19 Jun 2017
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini D. Song AAML 43 242 0 15 Jun 2017
Towards Robust Detection of Adversarial Examples Tianyu Pang Chao Du Yinpeng Dong Jun Zhu AAML 36 18 0 02 Jun 2017
Spectral Norm Regularization for Improving the Generalizability of Deep Learning Yuichi Yoshida Takeru Miyato 40 325 0 31 May 2017
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation Matthias Hein Maksym Andriushchenko AAML 45 505 0 23 May 2017
Regularizing deep networks using efficient layerwise adversarial training S. Sankaranarayanan Arpit Jain Rama Chellappa Ser Nam Lim AAML 30 96 0 22 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 61 1,842 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 65 2,699 0 19 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 48 1,352 0 18 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 86 798 0 28 Apr 2017
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato S. Maeda Masanori Koyama S. Ishii GAN 10 2,717 0 13 Apr 2017
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks Weilin Xu David Evans Yanjun Qi AAML 25 1,233 0 04 Apr 2017
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems Hossein Hosseini Yize Chen Sreeram Kannan Baosen Zhang Radha Poovendran AAML 30 106 0 13 Mar 2017
Dense Associative Memory is Robust to Adversarial Inputs Dmitry Krotov J. Hopfield AAML 23 111 0 04 Jan 2017
Simple Black-Box Adversarial Perturbations for Deep Networks Nina Narodytska S. Kasiviswanathan AAML 19 237 0 19 Dec 2016
Learning Adversary-Resistant Deep Neural Networks Qinglong Wang Wenbo Guo Kaixuan Zhang Alexander Ororbia Masashi Sugiyama Xue Liu C. Lee Giles AAML 18 43 0 05 Dec 2016
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches E. Rodner Marcel Simon Robert B. Fisher Joachim Denzler 17 39 0 21 Oct 2016
Adversary Resistant Deep Neural Networks with an Application to Malware Detection Qinglong Wang Wenbo Guo Kaixuan Zhang Alexander Ororbia Masashi Sugiyama C. Lee Giles Xue Liu AAML 19 173 0 05 Oct 2016
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 11 367 0 31 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 62 8,460 0 16 Aug 2016