Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
Authors: Rehana Mahfuz, Rajeev Sahay, Aly El Gamal | Published: 2020-01-26
Effectiveness Analysis of Defense Methods
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
Authors: Ying Meng, Jianhai Su, Jason O'Kane, Pooyan Jamshidi | Published: 2020-01-02 | Updated: 2020-10-16
Benchmarking Adversarial Robustness
Authors: Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu | Published: 2019-12-26
Explainability and Adversarial Robustness for RNNs
Authors: Alexander Hartl, Maximilian Bachl, Joachim Fabini, Tanja Zseby | Published: 2019-12-20 | Updated: 2020-02-19
A Survey of Black-Box Adversarial Attacks on Computer Vision Models
Authors: Siddhant Bhambri, Sumanyu Muku, Avinash Tulasi, Arun Balaji Buduru | Published: 2019-12-03 | Updated: 2020-02-07
One Man’s Trash is Another Man’s Treasure: Resisting Adversarial Examples by Adversarial Examples
Authors: Chang Xiao, Changxi Zheng | Published: 2019-11-25 | Updated: 2019-11-27
Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach
Authors: Iman Niazazari, Hanif Livani | Published: 2019-11-19 | Updated: 2019-11-25
Can You Really Backdoor Federated Learning?
Authors: Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan | Published: 2019-11-18 | Updated: 2019-12-02
Defending Against Model Stealing Attacks with Adaptive Misinformation
Authors: Sanjay Kariyappa, Moinuddin K Qureshi | Published: 2019-11-16
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks
Authors: Shai Rozenberg, Gal Elidan, Ran El-Yaniv | Published: 2019-11-03 | Updated: 2020-06-12