Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks
Authors: Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias | Published: 2020-09-30
ポイズニング
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients
Authors: Yifei Huang, Yaodong Yu, Hongyang Zhang, Yi Ma, Yuan Yao | Published: 2020-09-28 | Updated: 2021-06-02
A Robust graph attention network with dynamic adjusted Graph
Authors: Xianchen Zhou, Yaoyun Zeng, Hongxia Wang | Published: 2020-09-28 | Updated: 2022-08-04
Semantics-Preserving Adversarial Training
Authors: Wonseok Lee, Hanbit Lee, Sang-goo Lee | Published: 2020-09-23
Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)
Authors: Gautam Raj Mode, Khaza Anuarul Hoque | Published: 2020-09-21 | Updated: 2020-09-28
Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining
Authors: Łukasz Korycki, Bartosz Krawczyk | Published: 2020-09-20
Data Poisoning Attacks on Regression Learning and Corresponding Defenses
Authors: Nicolas Michael Müller, Daniel Kowatsch, Konstantin Böttinger | Published: 2020-09-15
Input Hessian Regularization of Neural Networks
Authors: Waleed Mustafa, Robert A. Vandermeulen, Marius Kloft | Published: 2020-09-14
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses
Authors: Ambar Pal, René Vidal | Published: 2020-09-14 | Updated: 2020-11-11
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent
Authors: Ricardo Bigolin Lanfredi, Joyce D. Schroeder, Tolga Tasdizen | Published: 2020-09-10 | Updated: 2023-04-20