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Towards Evaluating the Robustness of Neural Networks

16 August 2016

Papers citing "Towards Evaluating the Robustness of Neural Networks"

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Title
Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior Thomas Gittings Steve A. Schneider John Collomosse AAML 61 10 0 03 Jul 2019
Treant: Training Evasion-Aware Decision Trees Stefano Calzavara Claudio Lucchese Gabriele Tolomei S. Abebe S. Orlando AAML 75 41 0 02 Jul 2019
Diminishing the Effect of Adversarial Perturbations via Refining Feature Representation Nader Asadi Amirm. Sarfi Mehrdad Hosseinzadeh Sahba Tahsini M. Eftekhari AAML 32 2 0 01 Jul 2019
Accurate, reliable and fast robustness evaluation Wieland Brendel Jonas Rauber Matthias Kümmerer Ivan Ustyuzhaninov Matthias Bethge AAML OOD 97 113 0 01 Jul 2019
Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks M. Fornasier T. Klock Michael Rauchensteiner 83 18 0 30 Jun 2019
Fooling a Real Car with Adversarial Traffic Signs N. Morgulis Alexander Kreines Shachar Mendelowitz Yuval Weisglass AAML 89 93 0 30 Jun 2019
Cellular State Transformations using Generative Adversarial Networks Colin Targonski Benjamin T. Shealy M. C. Smith F. Feltus 21 1 0 28 Jun 2019
Robustness Guarantees for Deep Neural Networks on Videos Min Wu Marta Z. Kwiatkowska AAML 83 23 0 28 Jun 2019
Training individually fair ML models with Sensitive Subspace Robustness Mikhail Yurochkin Amanda Bower Yuekai Sun FaML OOD 95 120 0 28 Jun 2019
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense Guanxiong Liu Issa M. Khalil Abdallah Khreishah AAML 37 2 0 27 Jun 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 81 36 0 27 Jun 2019
Adversarial Robustness via Label-Smoothing Morgane Goibert Elvis Dohmatob AAML 124 18 0 27 Jun 2019
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness Fanny Yang Zuowen Wang C. Heinze-Deml 138 42 0 26 Jun 2019
Defending Adversarial Attacks by Correcting logits Yifeng Li Lingxi Xie Ya Zhang Rui Zhang Yanfeng Wang Qi Tian AAML 44 5 0 26 Jun 2019
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs Soheil Kolouri Aniruddha Saha Hamed Pirsiavash Heiko Hoffmann AAML 94 237 0 26 Jun 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 89 105 0 25 Jun 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 110 757 0 19 Jun 2019
Cloud-based Image Classification Service Is Not Robust To Simple Transformations: A Forgotten Battlefield Dou Goodman Tao Wei AAML 69 6 0 19 Jun 2019
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing Haonan Qiu Chaowei Xiao Lei Yang Xinchen Yan Honglak Lee Yue Liu AAML 71 172 0 19 Jun 2019
Global Adversarial Attacks for Assessing Deep Learning Robustness Hanbin Hu Mitt Shah Jianhua Z. Huang Peng Li AAML 80 4 0 19 Jun 2019
Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao Tianle Cai Haochuan Li Liwei Wang Cho-Jui Hsieh Jason D. Lee AAML 117 109 0 19 Jun 2019
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks F. Assion Peter Schlicht Florens Greßner W. Günther Fabian Hüger Nico M. Schmidt Umair Rasheed AAML 75 14 0 17 Jun 2019
Improving Black-box Adversarial Attacks with a Transfer-based Prior Shuyu Cheng Yinpeng Dong Tianyu Pang Hang Su Jun Zhu AAML 96 274 0 17 Jun 2019
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy Alex Lamb Vikas Verma Kenji Kawaguchi Alexander Matyasko Savya Khosla Arno Solin Yoshua Bengio AAML 74 100 0 16 Jun 2019
Defending Against Adversarial Attacks Using Random Forests Yifan Ding Liqiang Wang Huan Zhang Jinfeng Yi Deliang Fan Boqing Gong AAML 67 14 0 16 Jun 2019
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences Shashank Kotyan Danilo Vasconcellos Vargas Moe Matsuki 52 0 0 15 Jun 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 113 351 0 14 Jun 2019
Towards Compact and Robust Deep Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 82 40 0 14 Jun 2019
Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking Ziqi Yang Hung Dang E. Chang AAML 116 34 0 14 Jun 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 128 336 0 13 Jun 2019
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks R. Sahay Rehana Mahfuz Aly El Gamal AAML 33 5 0 13 Jun 2019
Evolutionary Trigger Set Generation for DNN Black-Box Watermarking Jiabao Guo M. Potkonjak AAML WIGM 63 15 0 11 Jun 2019
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks Ziang Yan Yiwen Guo Changshui Zhang AAML 81 111 0 11 Jun 2019
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong Xue Lin AAML 131 454 0 10 Jun 2019
E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles M. Kettunen Erik Härkönen J. Lehtinen AAML 65 63 0 10 Jun 2019
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective Lu Wang Xuanqing Liu Jinfeng Yi Zhi Zhou Cho-Jui Hsieh AAML 83 22 0 10 Jun 2019
Robustness Verification of Tree-based Models Hongge Chen Huan Zhang Si Si Yang Li Duane S. Boning Cho-Jui Hsieh AAML 103 77 0 10 Jun 2019
Attacking Graph Convolutional Networks via Rewiring Yao Ma Suhang Wang Tyler Derr Lingfei Wu Jiliang Tang AAML GNN 64 84 0 10 Jun 2019
On the Vulnerability of Capsule Networks to Adversarial Attacks Félix D. P. Michels Tobias Uelwer Eric Upschulte Stefan Harmeling AAML 77 24 0 09 Jun 2019
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad Yangqiu Song AAML 83 39 0 09 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks Maksym Andriushchenko Matthias Hein 84 62 0 08 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 98 101 0 08 Jun 2019
Defending Against Universal Attacks Through Selective Feature Regeneration Tejas S. Borkar Felix Heide Lina Karam AAML 52 1 0 08 Jun 2019
Making targeted black-box evasion attacks effective and efficient Mika Juuti B. Atli Nadarajah Asokan AAML MIACV MLAU 49 8 0 08 Jun 2019
Efficient Project Gradient Descent for Ensemble Adversarial Attack Fanyou Wu R. Gazo E. Haviarova Bedrich Benes AAML 33 5 0 07 Jun 2019
Robustness for Non-Parametric Classification: A Generic Attack and Defense Yao-Yuan Yang Cyrus Rashtchian Yizhen Wang Kamalika Chaudhuri SILM AAML 94 43 0 07 Jun 2019
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data Yan Li Ethan X. Fang Huan Xu T. Zhao 92 16 0 07 Jun 2019
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness Walt Woods Jack H Chen C. Teuscher AAML 73 46 0 07 Jun 2019
Query-efficient Meta Attack to Deep Neural Networks Jiawei Du Hu Zhang Qiufeng Wang Yi Yang Jiashi Feng AAML 66 84 0 06 Jun 2019
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise Xuanqing Liu Tesi Xiao Si Si Qin Cao Sanjiv Kumar Cho-Jui Hsieh 114 138 0 05 Jun 2019