Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder Approach
Authors: Rajeev Sahay, Rehana Mahfuz, Aly El Gamal | Published: 2018-12-07
文献データベース
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
Authors: Amir Moradibaad, Ramin Jalilian Mashhoud | Published: 2018-12-07 | Updated: 2018-12-13
Three Tools for Practical Differential Privacy
Authors: Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini | Published: 2018-12-07
Adversarial Attacks, Regression, and Numerical Stability Regularization
Authors: Andre T. Nguyen, Edward Raff | Published: 2018-12-07
Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase
Authors: Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Patrick Tague, Yuan Tian | Published: 2018-12-07
Knockoff Nets: Stealing Functionality of Black-Box Models
Authors: Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz | Published: 2018-12-06
The Limitations of Model Uncertainty in Adversarial Settings
Authors: Kathrin Grosse, David Pfaff, Michael Thomas Smith, Michael Backes | Published: 2018-12-06 | Updated: 2019-11-17
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
When Homomorphic Cryptosystem Meets Differential Privacy: Training Machine Learning Classifier with Privacy Protection
Authors: Xiangyun Tang, Liehuang Zhu, Meng Shen, Xiaojiang Du | Published: 2018-12-06