Towards Deep Learning Models Resistant to Adversarial Attacks

19 June 2017

Papers citing "Towards Deep Learning Models Resistant to Adversarial Attacks"

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Title
Non-Determinism in Neural Networks for Adversarial Robustness Daanish Ali Khan Linhong Li Ninghao Sha Zhuoran Liu Abelino Jiménez Bhiksha Raj Rita Singh OOD AAML 19 3 0 26 May 2019
Robust Classification using Robust Feature Augmentation Kevin Eykholt Swati Gupta Atul Prakash Amir Rahmati Pratik Vaishnavi Haizhong Zheng AAML 19 2 0 26 May 2019
Rearchitecting Classification Frameworks For Increased Robustness Varun Chandrasekaran Brian Tang Nicolas Papernot Kassem Fawaz S. Jha Xi Wu AAML OOD 42 8 0 26 May 2019
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling A. Bose Andre Cianflone William L. Hamilton OOD AAML 22 7 0 26 May 2019
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations Alex Lamb Jonathan Binas Anirudh Goyal Sandeep Subramanian Ioannis Mitliagkas Denis Kazakov Yoshua Bengio Michael C. Mozer OOD 19 3 0 26 May 2019
Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders Hebi Li Qi Xiao Shixin Tian Jin Tian AAML 27 4 0 26 May 2019
Adversarial Distillation for Ordered Top-k Attacks Zekun Zhang Tianfu Wu AAML 14 2 0 25 May 2019
Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao Peilin Zhong Changxi Zheng AAML 24 97 0 25 May 2019
Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks Yuanshun Yao Huiying Li Haitao Zheng Ben Y. Zhao AAML 35 13 0 24 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 6 235 0 24 May 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi S. Maeda Masanori Koyama Takeru Miyato OOD 32 129 0 24 May 2019
Robust Attribution Regularization Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha OOD 17 83 0 23 May 2019
Thwarting finite difference adversarial attacks with output randomization Haidar Khan Daniel Park Azer Khan B. Yener SILM AAML 41 0 0 23 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 28 158 0 23 May 2019
Adversarially Robust Distillation Micah Goldblum Liam H. Fowl S. Feizi Tom Goldstein AAML 15 201 0 23 May 2019
A Direct Approach to Robust Deep Learning Using Adversarial Networks Huaxia Wang Chun-Nam Yu GAN AAML OOD 24 77 0 23 May 2019
Convergence and Margin of Adversarial Training on Separable Data Zachary B. Charles Shashank Rajput S. Wright Dimitris Papailiopoulos AAML 34 16 0 22 May 2019
Adversarially robust transfer learning Ali Shafahi Parsa Saadatpanah Chen Zhu Amin Ghiasi Christoph Studer David Jacobs Tom Goldstein OOD 15 114 0 20 May 2019
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models Shubham Sharma Jette Henderson Joydeep Ghosh 11 87 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 37 18 0 19 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 5 0 19 May 2019
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning Z. Din P. Tigas Samuel T. King B. Livshits VLM 39 29 0 17 May 2019
Simple Black-box Adversarial Attacks Chuan Guo Jacob R. Gardner Yurong You A. Wilson Kilian Q. Weinberger AAML 28 568 0 17 May 2019
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models Nicholas Carlini ELM 25 14 0 17 May 2019
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization Seungyong Moon Gaon An Hyun Oh Song AAML MLAU 25 133 0 16 May 2019
On Norm-Agnostic Robustness of Adversarial Training Bai Li Changyou Chen Wenlin Wang Lawrence Carin OOD SILM 16 7 0 15 May 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu Wenjie Huang Ali Shafahi Hengduo Li Gavin Taylor Christoph Studer Tom Goldstein 38 283 0 15 May 2019
Adversarial Examples for Electrocardiograms Xintian Han Yuxuan Hu L. Foschini L. Chinitz Lior Jankelson Rajesh Ranganath AAML MedIm 11 4 0 13 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 19 61 0 13 May 2019
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks Olakunle Ibitoye Omair Shafiq Ashraf Matrawy 22 162 0 13 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 406 4,694 0 13 May 2019
Moving Target Defense for Deep Visual Sensing against Adversarial Examples Qun Song Zhenyu Yan Rui Tan AAML 21 20 0 11 May 2019
Interpreting and Evaluating Neural Network Robustness Fuxun Yu Zhuwei Qin Chenchen Liu Liang Zhao Yanzhi Wang Xiang Chen AAML 15 55 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 31 157 0 10 May 2019
Exploring the Hyperparameter Landscape of Adversarial Robustness Evelyn Duesterwald Anupama Murthi Ganesh Venkataraman M. Sinn Deepak Vijaykeerthy AAML 16 7 0 09 May 2019
Learning Interpretable Features via Adversarially Robust Optimization Ashkan Khakzar Shadi Albarqouni Nassir Navab MedIm FAtt 17 14 0 09 May 2019
Adversarial Defense Framework for Graph Neural Network Shen Wang Zhengzhang Chen Jingchao Ni Xiao Yu Zhichun Li Haifeng Chen Philip S. Yu AAML GNN 25 28 0 09 May 2019
ROSA: Robust Salient Object Detection against Adversarial Attacks Haofeng Li Guanbin Li Yizhou Yu AAML 16 28 0 09 May 2019
Does Data Augmentation Lead to Positive Margin? Shashank Rajput Zhili Feng Zachary B. Charles Po-Ling Loh Dimitris Papailiopoulos 21 37 0 08 May 2019
An Empirical Evaluation of Adversarial Robustness under Transfer Learning Todor Davchev Timos Korres Stathi Fotiadis N. Antonopoulos S. Ramamoorthy AAML 36 0 0 07 May 2019
Adaptive Generation of Unrestricted Adversarial Inputs Isaac Dunn Hadrien Pouget T. Melham Daniel Kroening AAML 28 7 0 07 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 57 1,810 0 06 May 2019
Batch Normalization is a Cause of Adversarial Vulnerability A. Galloway A. Golubeva T. Tanay M. Moussa Graham W. Taylor ODL AAML 25 80 0 06 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 32 26 0 05 May 2019
CharBot: A Simple and Effective Method for Evading DGA Classifiers Jonathan Peck Claire Nie R. Sivaguru Charles Grumer Femi G. Olumofin Bin Yu A. Nascimento Martine De Cock AAML 11 43 0 03 May 2019
Transfer of Adversarial Robustness Between Perturbation Types Daniel Kang Yi Sun Tom B. Brown Dan Hendrycks Jacob Steinhardt AAML 19 49 0 03 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 39 357 0 02 May 2019
Adversarial Training with Voronoi Constraints Marc Khoury Dylan Hadfield-Menell AAML 28 24 0 02 May 2019
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li Lijun Li Liqiang Wang Tong Zhang Boqing Gong AAML 18 245 0 01 May 2019
Dropping Pixels for Adversarial Robustness Hossein Hosseini Sreeram Kannan Radha Poovendran 14 16 0 01 May 2019