A Little Is Enough: Circumventing Defenses For Distributed Learning
Authors: Moran Baruch, Gilad Baruch, Yoav Goldberg | Published: 2019-02-16
敵対的攻撃
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
Authors: Silvija Kokalj-Filipovic, Rob Miller, Nicholas Chang, Chi Leung Lau | Published: 2019-02-16
Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustness
Authors: Silvija Kokalj-Filipovic, Rob Miller | Published: 2019-02-16
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Authors: Kevin Roth, Yannic Kilcher, Thomas Hofmann | Published: 2019-02-13 | Updated: 2019-05-09
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
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
Authors: Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann | Published: 2019-02-01 | Updated: 2019-03-09
The Efficacy of SHIELD under Different Threat Models
Authors: Cory Cornelius, Nilaksh Das, Shang-Tse Chen, Li Chen, Michael E. Kounavis, Duen Horng Chau | Published: 2019-02-01 | Updated: 2019-08-02
Robustness Certificates Against Adversarial Examples for ReLU Networks
Authors: Sahil Singla, Soheil Feizi | Published: 2019-02-01 | Updated: 2019-02-05
Optimal Attack against Autoregressive Models by Manipulating the Environment
Authors: Yiding Chen, Xiaojin Zhu | Published: 2019-02-01 | Updated: 2019-12-01