Explainability and Adversarial Robustness for RNNs
Authors: Alexander Hartl, Maximilian Bachl, Joachim Fabini, Tanja Zseby | Published: 2019-12-20 | Updated: 2020-02-19
防御手法の効果分析
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
Reducing audio membership inference attack accuracy to chance: 4 defenses
Authors: Michael Lomnitz, Nina Lopatina, Paul Gamble, Zigfried Hampel-Arias, Lucas Tindall, Felipe A. Mejia, Maria Alejandra Barrios | Published: 2019-10-31
MaskedNet: The First Hardware Inference Engine Aiming Power Side-Channel Protection
Authors: Anuj Dubey, Rosario Cammarota, Aydin Aysu | Published: 2019-10-29 | Updated: 2019-12-02
Understanding and Quantifying Adversarial Examples Existence in Linear Classification
Authors: Xupeng Shi, A. Adam Ding | Published: 2019-10-27