Towards Evaluating the Robustness of Neural Networks

16 August 2016

Papers citing "Towards Evaluating the Robustness of Neural Networks"

50 / 1,570 papers shown

Title
Verification of Non-Linear Specifications for Neural Networks Chongli Qin Krishnamurthy Dvijotham Dvijotham Brendan O'Donoghue Rudy Bunel Robert Stanforth Sven Gowal J. Uesato G. Swirszcz Pushmeet Kohli AAML 14 43 0 25 Feb 2019
Batch Virtual Adversarial Training for Graph Convolutional Networks Zhijie Deng Yinpeng Dong Jun Zhu GNN 28 63 0 25 Feb 2019
MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses Lior Sidi Asaf Nadler A. Shabtai AAML 28 22 0 24 Feb 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks Hadi Salman Greg Yang Huan Zhang Cho-Jui Hsieh Pengchuan Zhang AAML 21 263 0 23 Feb 2019
Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment Ziqi Yang E. Chang Zhenkai Liang MLAU 33 60 0 22 Feb 2019
On the Sensitivity of Adversarial Robustness to Input Data Distributions G. Ding Kry Yik-Chau Lui Xiaomeng Jin Luyu Wang Ruitong Huang OOD 26 59 0 22 Feb 2019
Quantifying Perceptual Distortion of Adversarial Examples Matt Jordan N. Manoj Surbhi Goel A. Dimakis 19 39 0 21 Feb 2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations Eric Wong Frank R. Schmidt J. Zico Kolter AAML 27 210 0 21 Feb 2019
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training S. Kokalj-Filipovic Rob Miller Nicholas Chang Chi Leung Lau AAML 14 36 0 16 Feb 2019
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples Kevin Roth Yannic Kilcher Thomas Hofmann AAML 27 175 0 13 Feb 2019
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples Derui Wang Chaoran Li S. Wen Qing-Long Han Surya Nepal Xiangyu Zhang Yang Xiang AAML 30 40 0 06 Feb 2019
Collaborative Sampling in Generative Adversarial Networks Yuejiang Liu Parth Kothari Alexandre Alahi TTA 28 16 0 02 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 22 59 0 01 Feb 2019
A New Family of Neural Networks Provably Resistant to Adversarial Attacks Rakshit Agrawal Luca de Alfaro D. Helmbold AAML OOD 27 2 0 01 Feb 2019
RED-Attack: Resource Efficient Decision based Attack for Machine Learning Faiq Khalid Hassan Ali Muhammad Abdullah Hanif Semeen Rehman Rehan Ahmed Muhammad Shafique AAML 31 14 0 29 Jan 2019
Weighted-Sampling Audio Adversarial Example Attack Xiaolei Liu Xiaosong Zhang Kun Wan Qingxin Zhu Yufei Ding DiffM AAML 36 36 0 26 Jan 2019
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm Xiaolei Liu Yuheng Luo Xiaosong Zhang Qingxin Zhu AAML 24 16 0 26 Jan 2019
Improving Adversarial Robustness via Promoting Ensemble Diversity Tianyu Pang Kun Xu Chao Du Ning Chen Jun Zhu AAML 41 434 0 25 Jan 2019
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples Kamil Nar Orhan Ocal S. Shankar Sastry Kannan Ramchandran AAML 27 54 0 24 Jan 2019
Universal Rules for Fooling Deep Neural Networks based Text Classification Di Li Danilo Vasconcellos Vargas Kouichi Sakurai AAML 21 11 0 22 Jan 2019
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey W. Zhang Quan Z. Sheng A. Alhazmi Chenliang Li AAML 24 57 0 21 Jan 2019
Optimization Problems for Machine Learning: A Survey Claudio Gambella Bissan Ghaddar Joe Naoum-Sawaya AI4CE 30 178 0 16 Jan 2019
The Limitations of Adversarial Training and the Blind-Spot Attack Huan Zhang Hongge Chen Zhao Song Duane S. Boning Inderjit S. Dhillon Cho-Jui Hsieh AAML 19 144 0 15 Jan 2019
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System Huangxun Chen Chenyu Huang Qianyi Huang Qian Zhang Wei Wang AAML 31 26 0 12 Jan 2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 24 129 0 11 Jan 2019
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification L. G. Hafemann R. Sabourin Luiz Eduardo Soares de Oliveira AAML 11 42 0 10 Jan 2019
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers Daniel Liu Ronald Yu Hao Su 3DPC 34 165 0 10 Jan 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 17 74 0 08 Jan 2019
Image Super-Resolution as a Defense Against Adversarial Attacks Aamir Mustafa Salman H. Khan Munawar Hayat Jianbing Shen Ling Shao AAML SupR 24 167 0 07 Jan 2019
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study Xurong Li S. Ji Men Han Juntao Ji Zhenyu Ren Yushan Liu Chunming Wu AAML 23 31 0 04 Jan 2019
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack Haishan Ye Zhichao Huang Cong Fang C. J. Li Tong Zhang AAML 18 41 0 29 Dec 2018
Adversarial Attack and Defense on Graph Data: A Survey Lichao Sun Yingtong Dou Carl Yang Ji Wang Yixin Liu Philip S. Yu Lifang He Yangqiu Song GNN AAML 23 275 0 26 Dec 2018
Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks Xiang Li Shihao Ji AAML 27 26 0 17 Dec 2018
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 55 553 0 13 Dec 2018
TextBugger: Generating Adversarial Text Against Real-world Applications Jinfeng Li S. Ji Tianyu Du Bo Li Ting Wang SILM AAML 72 723 0 13 Dec 2018
Thwarting Adversarial Examples: An $L_0$ -RobustSparse Fourier Transform Mitali Bafna Jack Murtagh Nikhil Vyas AAML 11 48 0 12 Dec 2018
Adversarial Framing for Image and Video Classification Konrad Zolna Michal Zajac Negar Rostamzadeh Pedro H. O. Pinheiro AAML 30 60 0 11 Dec 2018
On the Security of Randomized Defenses Against Adversarial Samples K. Sharad G. Marson H. Truong Ghassan O. Karame AAML 25 1 0 11 Dec 2018
Data Fine-tuning S. Chhabra P. Majumdar Mayank Vatsa Richa Singh AAML 20 13 0 10 Dec 2018
Defending Against Universal Perturbations With Shared Adversarial Training Chaithanya Kumar Mummadi Thomas Brox J. H. Metzen AAML 18 60 0 10 Dec 2018
Learning Transferable Adversarial Examples via Ghost Networks Yingwei Li S. Bai Yuyin Zhou Cihang Xie Zhishuai Zhang Alan Yuille AAML 39 136 0 09 Dec 2018
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach R. Sahay Rehana Mahfuz Aly El Gamal 17 33 0 07 Dec 2018
Adversarial Defense of Image Classification Using a Variational Auto-Encoder Yi-Si Luo H. Pfister AAML 11 9 0 07 Dec 2018
MMA Training: Direct Input Space Margin Maximization through Adversarial Training G. Ding Yash Sharma Kry Yik-Chau Lui Ruitong Huang AAML 21 270 0 06 Dec 2018
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples Huangyi Ge Sze Yiu Chau Bruno Ribeiro Ninghui Li AAML 27 1 0 05 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 24 169 0 03 Dec 2018
Universal Perturbation Attack Against Image Retrieval Jie Li Rongrong Ji Hong Liu Xiaopeng Hong Yue Gao Q. Tian AAML 29 98 0 03 Dec 2018
Model-Reuse Attacks on Deep Learning Systems Yujie Ji Xinyang Zhang S. Ji Xiapu Luo Ting Wang SILM AAML 134 186 0 02 Dec 2018
Deep Learning Application in Security and Privacy -- Theory and Practice: A Position Paper Julia A. Meister Raja Naeem Akram K. Markantonakis 18 0 0 01 Dec 2018
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification Qi Lei Lingfei Wu Pin-Yu Chen A. Dimakis Inderjit S. Dhillon Michael Witbrock AAML 15 92 0 01 Dec 2018