Towards Deep Learning Models Resistant to Adversarial Attacks

19 June 2017

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

50 / 6,519 papers shown

Title
Image Decomposition and Classification through a Generative Model Houpu Yao Malcolm Regan Yezhou Yang Yi Ren GAN 9 1 0 09 Feb 2019
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images S. Srivastava Guy Ben-Yosef Xavier Boix AAML 30 27 0 08 Feb 2019
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks Priyadarshini Panda I. Chakraborty Kaushik Roy AAML 25 40 0 08 Feb 2019
Understanding the One-Pixel Attack: Propagation Maps and Locality Analysis Danilo Vasconcellos Vargas Jiawei Su FAtt AAML 11 36 0 08 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 22 1,998 0 08 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
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 27 201 0 06 Feb 2019
Are All Layers Created Equal? Chiyuan Zhang Samy Bengio Y. Singer 20 140 0 06 Feb 2019
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss Nicholas Frosst Nicolas Papernot Geoffrey E. Hinton 17 157 0 05 Feb 2019
Theoretical evidence for adversarial robustness through randomization Rafael Pinot Laurent Meunier Alexandre Araujo H. Kashima Florian Yger Cédric Gouy-Pailler Jamal Atif AAML 47 82 0 04 Feb 2019
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks Alberto Marchisio Giorgio Nanfa Faiq Khalid Muhammad Abdullah Hanif Maurizio Martina Mohamed Bennai AAML 13 7 0 04 Feb 2019
Collaborative Sampling in Generative Adversarial Networks Yuejiang Liu Parth Kothari Alexandre Alahi TTA 30 16 0 02 Feb 2019
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? Chi Jin Praneeth Netrapalli Michael I. Jordan 21 82 0 02 Feb 2019
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks S. Saralajew Lars Holdijk Maike Rees T. Villmann OOD 25 19 0 01 Feb 2019
The Efficacy of SHIELD under Different Threat Models Cory Cornelius Nilaksh Das Shang-Tse Chen Li Chen Michael E. Kounavis Duen Horng Chau AAML 18 11 0 01 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 34 59 0 01 Feb 2019
Robustness Certificates Against Adversarial Examples for ReLU Networks Sahil Singla S. Feizi AAML 25 21 0 01 Feb 2019
Natural and Adversarial Error Detection using Invariance to Image Transformations Yuval Bahat Michal Irani Gregory Shakhnarovich AAML 9 18 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
Augmenting Model Robustness with Transformation-Invariant Attacks Houpu Yao Zhe Wang Guangyu Nie Yassine Mazboudi Yezhou Yang Yi Ren AAML OOD 11 3 0 31 Jan 2019
HyperGAN: A Generative Model for Diverse, Performant Neural Networks Neale Ratzlaff Fuxin Li 20 63 0 30 Jan 2019
A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance A. Shamir Itay Safran Eyal Ronen O. Dunkelman GAN AAML 11 94 0 30 Jan 2019
Reliable Smart Road Signs M. O. Sayin Chung-Wei Lin Eunsuk Kang Shiníchi Shiraishi Tamer Basar AAML 11 0 0 30 Jan 2019
Adversarial Examples Are a Natural Consequence of Test Error in Noise Nic Ford Justin Gilmer Nicholas Carlini E. D. Cubuk AAML 36 318 0 29 Jan 2019
On the Effect of Low-Rank Weights on Adversarial Robustness of Neural Networks P. Langenberg E. Balda Arash Behboodi R. Mathar 11 16 0 29 Jan 2019
Improving Adversarial Robustness of Ensembles with Diversity Training Sanjay Kariyappa Moinuddin K. Qureshi AAML FedML 17 133 0 28 Jan 2019
Defense Methods Against Adversarial Examples for Recurrent Neural Networks Ishai Rosenberg A. Shabtai Yuval Elovici Lior Rokach AAML GAN 32 42 0 28 Jan 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 34 721 0 28 Jan 2019
Characterizing the Shape of Activation Space in Deep Neural Networks Thomas Gebhart Paul Schrater Alan Hylton AAML 11 7 0 28 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 39 449 0 27 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
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 69 2,500 0 24 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
A review of domain adaptation without target labels Wouter M. Kouw Marco Loog OOD VLM 15 478 0 16 Jan 2019
Optimization Problems for Machine Learning: A Survey Claudio Gambella Bissan Ghaddar Joe Naoum-Sawaya AI4CE 37 178 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 22 144 0 15 Jan 2019
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification L. G. Hafemann R. Sabourin Luiz Eduardo Soares de Oliveira AAML 19 42 0 10 Jan 2019
Image Transformation can make Neural Networks more robust against Adversarial Examples D. D. Thang Toshihiro Matsui AAML 11 10 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
Image Super-Resolution as a Defense Against Adversarial Attacks Aamir Mustafa Salman H. Khan Munawar Hayat Jianbing Shen Ling Shao AAML SupR 27 168 0 07 Jan 2019
Adversarial CAPTCHAs Chenghui Shi Xiaogang Xu S. Ji Kai Bu Jianhai Chen R. Beyah Ting Wang AAML 22 52 0 04 Jan 2019
Adversarial Robustness May Be at Odds With Simplicity Preetum Nakkiran AAML 14 105 0 02 Jan 2019
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
Towards a Theoretical Understanding of Hashing-Based Neural Nets Yibo Lin Zhao Song Lin F. Yang 22 5 0 26 Dec 2018
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning Mehdi Jafarnia-Jahromi Tasmin Chowdhury Hsin-Tai Wu S. Mukherjee AAML 27 4 0 25 Dec 2018
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks T. Brunner Frederik Diehl Michael Truong-Le Alois Knoll MLAU AAML 17 116 0 24 Dec 2018
Increasing the adversarial robustness and explainability of capsule networks with $γ$ -capsules David Peer Sebastian Stabinger A. Rodríguez-Sánchez AAML GAN MedIm 39 11 0 23 Dec 2018
Exploiting the Inherent Limitation of L0 Adversarial Examples F. Zuo Bokai Yang Xiaopeng Li Lannan Luo Qiang Zeng AAML 29 1 0 23 Dec 2018
Towards resilient machine learning for ransomware detection Li-Wei Chen Chih-Yuan Yang Anindya Paul R. Sahita AAML 14 22 0 21 Dec 2018
Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge Deqiang Li Qianmu Li Yanfang Ye Shouhuai Xu AAML 19 15 0 19 Dec 2018