Hack The Box: Fooling Deep Learning Abstraction-Based Monitors
Authors: Sara Hajj Ibrahim, Mohamed Nassar | Published: 2021-07-10 | Updated: 2021-07-18
敵対的サンプル
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Authors: Jon Vadillo, Roberto Santana, Jose A. Lozano | Published: 2021-07-05 | Updated: 2023-07-07
On the (Un-)Avoidability of Adversarial Examples
Authors: Sadia Chowdhury, Ruth Urner | Published: 2021-06-24
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Authors: Maura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli | Published: 2021-06-18 | Updated: 2022-10-11
Localized Uncertainty Attacks
Authors: Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas, Cristian Canton Ferrer, Ilknur Kaynar Kabul, Erik Meijer | Published: 2021-06-17
Adversarial Examples Detection with Bayesian Neural Network
Authors: Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee | Published: 2021-05-18 | Updated: 2024-02-23
Mental Models of Adversarial Machine Learning
Authors: Lukas Bieringer, Kathrin Grosse, Michael Backes, Battista Biggio, Katharina Krombholz | Published: 2021-05-08 | Updated: 2022-06-29
Who’s Afraid of Adversarial Transferability?
Authors: Ziv Katzir, Yuval Elovici | Published: 2021-05-02 | Updated: 2022-10-06
Adversarial Training for Deep Learning-based Intrusion Detection Systems
Authors: Islam Debicha, Thibault Debatty, Jean-Michel Dricot, Wim Mees | Published: 2021-04-20
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
Authors: Yue Gao, Ilia Shumailov, Kassem Fawaz | Published: 2021-04-18 | Updated: 2022-06-19