Ensemble Adversarial Training: Attacks and Defenses

19 May 2017

Dan Boneh

Papers citing "Ensemble Adversarial Training: Attacks and Defenses"

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Title
Security Matters: A Survey on Adversarial Machine Learning Guofu Li Pengjia Zhu Jin Li Zhemin Yang Ning Cao Zhiyi Chen AAML 23 24 0 16 Oct 2018
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation Chaowei Xiao Ruizhi Deng Bo-wen Li Feng Yu M. Liu D. Song AAML 16 99 0 11 Oct 2018
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only T. Zheng Changyou Chen K. Ren AAML 18 6 0 10 Oct 2018
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks Sanghyun Hong Michael Davinroy Yigitcan Kaya S. Locke Ian Rackow Kevin Kulda Dana Dachman-Soled Tudor Dumitras MIACV 25 90 0 08 Oct 2018
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness Chihuang Liu J. JáJá AAML 18 12 0 04 Oct 2018
Adversarial Examples - A Complete Characterisation of the Phenomenon A. Serban E. Poll Joost Visser SILM AAML 27 49 0 02 Oct 2018
Improved robustness to adversarial examples using Lipschitz regularization of the loss Chris Finlay Adam M. Oberman B. Abbasi 24 34 0 01 Oct 2018
Improving the Generalization of Adversarial Training with Domain Adaptation Chuanbiao Song Kun He Liwei Wang J. Hopcroft AAML OOD 17 131 0 01 Oct 2018
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks Kenneth T. Co Luis Muñoz-González Sixte de Maupeou Emil C. Lupu AAML 22 67 0 30 Sep 2018
CAAD 2018: Generating Transferable Adversarial Examples Yash Sharma Tien-Dung Le M. Alzantot AAML SILM 18 7 0 29 Sep 2018
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 18 0 0 29 Sep 2018
Adversarial Attacks and Defences: A Survey Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay Debdeep Mukhopadhyay AAML OOD 8 673 0 28 Sep 2018
Neural Networks with Structural Resistance to Adversarial Attacks Luca de Alfaro AAML 6 5 0 25 Sep 2018
Fast Geometrically-Perturbed Adversarial Faces Ali Dabouei Sobhan Soleymani J. Dawson Nasser M. Nasrabadi CVBM AAML 26 65 0 24 Sep 2018
Low Frequency Adversarial Perturbation Chuan Guo Jared S. Frank Kilian Q. Weinberger AAML 19 164 0 24 Sep 2018
Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization Bao Wang A. Lin Weizhi Zhu Penghang Yin Andrea L. Bertozzi Stanley J. Osher AAML 29 21 0 23 Sep 2018
Playing the Game of Universal Adversarial Perturbations Julien Perolat Mateusz Malinowski Bilal Piot Olivier Pietquin AAML 11 24 0 20 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 22 21 0 18 Sep 2018
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks Siyue Wang Tianlin Li Pu Zhao Wujie Wen David Kaeli S. Chin X. Lin AAML 16 70 0 13 Sep 2018
Adversarial Examples: Opportunities and Challenges Jiliang Zhang Chen Li AAML 9 233 0 13 Sep 2018
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions Yusuke Tsuzuku Issei Sato AAML 16 62 0 11 Sep 2018
Certified Adversarial Robustness with Additive Noise Bai Li Changyou Chen Wenlin Wang Lawrence Carin AAML 28 341 0 10 Sep 2018
Are adversarial examples inevitable? Ali Shafahi Yifan Jiang Christoph Studer S. Feizi Tom Goldstein SILM 11 280 0 06 Sep 2018
Bridging machine learning and cryptography in defence against adversarial attacks O. Taran Shideh Rezaeifar Slava Voloshynovskiy AAML 13 22 0 05 Sep 2018
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing Xiaofei Xie L. Ma Felix Juefei Xu Hongxu Chen Minhui Xue Bo-wen Li Yang Liu Jianjun Zhao Jianxiong Yin Simon See 37 40 0 04 Sep 2018
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks Siwakorn Srisakaokul Yuhao Zhang Zexuan Zhong Wei Yang Tao Xie Bo Li AAML 14 19 0 31 Aug 2018
Maximal Jacobian-based Saliency Map Attack R. Wiyatno Anqi Xu AAML 6 87 0 23 Aug 2018
Are You Tampering With My Data? Michele Alberti Vinaychandran Pondenkandath Marcel Würsch Manuel Bouillon Mathias Seuret Rolf Ingold Marcus Liwicki AAML 37 19 0 21 Aug 2018
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection Mahdieh Abbasi Arezoo Rajabi A. Mozafari R. Bobba Christian Gagné AAML 24 9 0 21 Aug 2018
Reinforcement Learning for Autonomous Defence in Software-Defined Networking Yi Han Benjamin I. P. Rubinstein Tamas Abraham T. Alpcan O. Vel S. Erfani David Hubczenko C. Leckie Paul Montague AAML 14 68 0 17 Aug 2018
Mitigation of Adversarial Attacks through Embedded Feature Selection Ziyi Bao Luis Muñoz-González Emil C. Lupu AAML 17 1 0 16 Aug 2018
Distributionally Adversarial Attack T. Zheng Changyou Chen K. Ren OOD 13 121 0 16 Aug 2018
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer Hsueh-Ti Derek Liu Michael Tao Chun-Liang Li Derek Nowrouzezahrai Alec Jacobson AAML 33 13 0 08 Aug 2018
Adversarial Vision Challenge Wieland Brendel Jonas Rauber Alexey Kurakin Nicolas Papernot Behar Veliqi M. Salathé Sharada Mohanty Matthias Bethge AAML 16 58 0 06 Aug 2018
Defense Against Adversarial Attacks with Saak Transform Sibo Song Yueru Chen Ngai-man Cheung C.-C. Jay Kuo 20 24 0 06 Aug 2018
Gray-box Adversarial Training S. VivekB. Konda Reddy Mopuri R. Venkatesh Babu AAML 8 34 0 06 Aug 2018
Enabling Trust in Deep Learning Models: A Digital Forensics Case Study Aditya K Slawomir Grzonkowski NhienAn Lekhac 11 27 0 03 Aug 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
Hardware Trojan Attacks on Neural Networks Joseph Clements Yingjie Lao AAML 11 89 0 14 Jun 2018
Re-evaluating Evaluation David Balduzzi K. Tuyls Julien Perolat T. Graepel MoMe 16 97 0 07 Jun 2018
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models A. Salem Yang Zhang Mathias Humbert Pascal Berrang Mario Fritz Michael Backes MIACV MIALM 34 926 0 04 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
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks Chun-Chen Tu Pai-Shun Ting Pin-Yu Chen Sijia Liu Huan Zhang Jinfeng Yi Cho-Jui Hsieh Shin-Ming Cheng MLAU AAML 13 394 0 30 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
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 26 1,163 0 17 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
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar A. Madry OOD AAML 23 784 0 30 Apr 2018
VectorDefense: Vectorization as a Defense to Adversarial Examples V. Kabilan Brandon L. Morris Anh Totti Nguyen AAML 19 21 0 23 Apr 2018
Decoupled Networks Weiyang Liu Ziqiang Liu Zhiding Yu Bo Dai Rongmei Lin Yisen Wang James M. Rehg Le Song OOD 17 5 0 22 Apr 2018
ADef: an Iterative Algorithm to Construct Adversarial Deformations Rima Alaifari Giovanni S. Alberti Tandri Gauksson AAML 14 96 0 20 Apr 2018