A Little Is Enough: Circumventing Defenses For Distributed Learning
Authors: Moran Baruch, Gilad Baruch, Yoav Goldberg | Published: 2019-02-16
敵対的学習
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Authors: Shupeng Gui, Haotao Wang, Chen Yu, Haichuan Yang, Zhangyang Wang, Ji Liu | Published: 2019-02-10 | Updated: 2019-12-28
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks
Authors: Alberto Marchisio, Giorgio Nanfa, Faiq Khalid, Muhammad Abdullah Hanif, Maurizio Martina, Muhammad Shafique | Published: 2019-02-04 | Updated: 2020-05-18
A New Family of Neural Networks Provably Resistant to Adversarial Attacks
Authors: Rakshit Agrawal, Luca de Alfaro, David Helmbold | Published: 2019-02-01
Improving Adversarial Robustness via Promoting Ensemble Diversity
Authors: Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu | Published: 2019-01-25 | Updated: 2019-05-29
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
Authors: Mehdi Jafarnia-Jahromi, Tasmin Chowdhury, Hsin-Tai Wu, Sayandev Mukherjee | Published: 2018-12-25 | Updated: 2020-01-04
Trust Region Based Adversarial Attack on Neural Networks
Authors: Zhewei Yao, Amir Gholami, Peng Xu, Kurt Keutzer, Michael Mahoney | Published: 2018-12-16
Prior Networks for Detection of Adversarial Attacks
Authors: Andrey Malinin, Mark Gales | Published: 2018-12-06
On Configurable Defense against Adversarial Example Attacks
Authors: Bo Luo, Min Li, Yu Li, Qiang Xu | Published: 2018-12-06
Model-Reuse Attacks on Deep Learning Systems
Authors: Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, Ting Wang | Published: 2018-12-02