Adversarial Examples in Modern Machine Learning: A Review
Authors: Rey Reza Wiyatno, Anqi Xu, Ousmane Dia, Archy de Berker | Published: 2019-11-13 | Updated: 2019-11-15
敵対的サンプル
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Authors: Arash Rahnama, Andre T. Nguyen, Edward Raff | Published: 2019-11-12
Imperceptible Adversarial Attacks on Tabular Data
Authors: Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, Marcin Detyniecki | Published: 2019-11-08 | Updated: 2019-12-13
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models
Authors: Ren Pang, Hua Shen, Xinyang Zhang, Shouling Ji, Yevgeniy Vorobeychik, Xiapu Luo, Alex Liu, Ting Wang | Published: 2019-11-05 | Updated: 2020-11-21
Fast-UAP: An Algorithm for Speeding up Universal Adversarial Perturbation Generation with Orientation of Perturbation Vectors
Authors: Jiazhu Dai, Le Shu | Published: 2019-11-04 | Updated: 2020-01-06
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks
Authors: Shai Rozenberg, Gal Elidan, Ran El-Yaniv | Published: 2019-11-03 | Updated: 2020-06-12
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization
Authors: Rana Abou Khamis, Omair Shafiq, Ashraf Matrawy | Published: 2019-10-30
Understanding and Quantifying Adversarial Examples Existence in Linear Classification
Authors: Xupeng Shi, A. Adam Ding | Published: 2019-10-27
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
Authors: Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein | Published: 2019-10-25
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
Authors: Alexander Levine, Soheil Feizi | Published: 2019-10-23