Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Authors: Eric Wong, Frank R. Schmidt, J. Zico Kolter | Published: 2019-02-21 | Updated: 2020-01-18
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
Authors: Gavin Weiguang Ding, Luyu Wang, Xiaomeng Jin | Published: 2019-02-20
There are No Bit Parts for Sign Bits in Black-Box Attacks
Authors: Abdullah Al-Dujaili, Una-May O'Reilly | Published: 2019-02-19 | Updated: 2019-04-03
On Evaluating Adversarial Robustness
Authors: Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin | Published: 2019-02-18 | Updated: 2019-02-20
Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces
Authors: Mohammad Saidur Rahman, Mohsen Imani, Nate Mathews, Matthew Wright | Published: 2019-02-18 | Updated: 2020-10-28
Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach. A Case Study on Automatic Classification of Global Terrorist Attacks
Authors: Shadi Diab | Published: 2019-02-18 | Updated: 2019-02-23
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Authors: Yansong Gao, Chang Xu, Derui Wang, Shiping Chen, Damith C. Ranasinghe, Surya Nepal | Published: 2019-02-18 | Updated: 2020-01-17
A Little Is Enough: Circumventing Defenses For Distributed Learning
Authors: Moran Baruch, Gilad Baruch, Yoav Goldberg | Published: 2019-02-16
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