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Towards Evaluating the Robustness of Neural Networks

16 August 2016

Papers citing "Towards Evaluating the Robustness of Neural Networks"

50 / 4,017 papers shown

Title
Evaluating Explanation Methods for Deep Learning in Security Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 71 94 0 05 Jun 2019
Multi-way Encoding for Robustness Donghyun Kim Sarah Adel Bargal Jianming Zhang Stan Sclaroff AAML 41 2 0 05 Jun 2019
Understanding the Limitations of Conditional Generative Models Ethan Fetaya J. Jacobsen Will Grathwohl R. Zemel 98 54 0 04 Jun 2019
Architecture Selection via the Trade-off Between Accuracy and Robustness Zhun Deng Cynthia Dwork Jialiang Wang Yao-Min Zhao AAML 98 3 0 04 Jun 2019
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks? Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha M. Hamdi AAML 56 19 0 03 Jun 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He John E. Hopcroft Liwei Wang 98 158 0 03 Jun 2019
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness Nhathai Phan Minh Nhat Vu Yang Liu R. Jin Dejing Dou Xintao Wu My T. Thai AAML 69 51 0 02 Jun 2019
Enhancing Transformation-based Defenses using a Distribution Classifier C. Kou H. Lee E. Chang Teck Khim Ng 69 3 0 01 Jun 2019
Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification Sid Ahmed Fezza Yassine Bakhti W. Hamidouche Olivier Déforges AAML 57 33 0 01 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 147 754 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 97 335 0 31 May 2019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness A. Malinin Mark Gales UQCV AAML 95 178 0 31 May 2019
Bypassing Backdoor Detection Algorithms in Deep Learning T. Tan Reza Shokri FedML AAML 111 152 0 31 May 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward A. Qayyum Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha AAML 94 191 0 29 May 2019
Misleading Authorship Attribution of Source Code using Adversarial Learning Erwin Quiring Alwin Maier Konrad Rieck 65 108 0 29 May 2019
Functional Adversarial Attacks Cassidy Laidlaw Soheil Feizi AAML 100 185 0 29 May 2019
CopyCAT: Taking Control of Neural Policies with Constant Attacks Léonard Hussenot Matthieu Geist Olivier Pietquin AAML 54 31 0 29 May 2019
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness Saeed Mahloujifar Xiao Zhang Mohammad Mahmoody David Evans 67 22 0 29 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla Soheil Feizi FAtt AAML 104 65 0 28 May 2019
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks Haohan Wang Xindi Wu Pengcheng Yin Eric Xing 91 528 0 28 May 2019
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation Yuzhe Yang Guo Zhang Dina Katabi Zhi Xu AAML 100 171 0 28 May 2019
Snooping Attacks on Deep Reinforcement Learning Matthew J. Inkawhich Yiran Chen Hai Helen Li AAML 68 25 0 28 May 2019
Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics Y. Tan Alfonso Iacovazzi I. Homoliak Yuval Elovici Alexander Binder AAML 44 24 0 28 May 2019
Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss Pengcheng Li Jinfeng Yi Bowen Zhou Lijun Zhang AAML 65 37 0 28 May 2019
Adversarially Robust Learning Could Leverage Computational Hardness Sanjam Garg S. Jha Saeed Mahloujifar Mohammad Mahmoody AAML 163 24 0 28 May 2019
Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks Pu Zhao Siyue Wang Cheng Gongye Yanzhi Wang Yunsi Fei Xinyu Lin AAML 64 76 0 28 May 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 106 44 0 27 May 2019
Scaleable input gradient regularization for adversarial robustness Chris Finlay Adam M. Oberman AAML 101 79 0 27 May 2019
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling A. Bose Andre Cianflone William L. Hamilton OOD AAML 75 7 0 26 May 2019
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders Hebi Li Qi Xiao Shixin Tian Jin Tian AAML 68 4 0 26 May 2019
Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao Peilin Zhong Changxi Zheng AAML 84 99 0 25 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 94 249 0 24 May 2019
Thwarting finite difference adversarial attacks with output randomization Haidar Khan Daniel Park Azer Khan B. Yener SILM AAML 52 0 0 23 May 2019
Adversarially Robust Distillation Micah Goldblum Liam H. Fowl Soheil Feizi Tom Goldstein AAML 96 213 0 23 May 2019
A Direct Approach to Robust Deep Learning Using Adversarial Networks Huaxia Wang Chun-Nam Yu GAN AAML OOD 76 77 0 23 May 2019
A framework for the extraction of Deep Neural Networks by leveraging public data Soham Pal Yash Gupta Aditya Shukla Aditya Kanade S. Shevade V. Ganapathy FedML MLAU MIACV 87 56 0 22 May 2019
Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating Giulio Lovisotto Simon Eberz Ivan Martinovic AAML 88 36 0 22 May 2019
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks Zirui Xu Fuxun Yu Xiang Chen AAML 47 0 0 21 May 2019
Testing DNN Image Classifiers for Confusion & Bias Errors Yuchi Tian Ziyuan Zhong Vicente Ordonez Gail E. Kaiser Baishakhi Ray 153 53 0 20 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain Lei Bu Yuchao Duan Fu Song Zhe Zhao AAML 114 18 0 19 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 32 5 0 19 May 2019
POPQORN: Quantifying Robustness of Recurrent Neural Networks Ching-Yun Ko Zhaoyang Lyu Tsui-Wei Weng Luca Daniel Ngai Wong Dahua Lin AAML 69 76 0 17 May 2019
Simple Black-box Adversarial Attacks Chuan Guo Jacob R. Gardner Yurong You A. Wilson Kilian Q. Weinberger AAML 80 583 0 17 May 2019
Robustification of deep net classifiers by key based diversified aggregation with pre-filtering O. Taran Shideh Rezaeifar T. Holotyak Svyatoslav Voloshynovskiy AAML 59 1 0 14 May 2019
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models M. Singh Abhishek Sinha Nupur Kumari Harshitha Machiraju Balaji Krishnamurthy V. Balasubramanian AAML 56 61 0 13 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 50 21 0 11 May 2019
Interpreting and Evaluating Neural Network Robustness Fuxun Yu Zhuwei Qin Chenchen Liu Liang Zhao Yanzhi Wang Xiang Chen AAML 60 56 0 10 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 71 162 0 10 May 2019
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables Yan Xu Baoyuan Wu Fumin Shen Yanbo Fan Yong Zhang Heng Tao Shen Wei Liu AAML 78 56 0 10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness Evelyn Duesterwald Anupama Murthi Ganesh Venkataraman M. Sinn Deepak Vijaykeerthy AAML 52 7 0 09 May 2019