Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning

20 February 2018

Papers citing "Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning"

8 / 8 papers shown

Title
Timber! Poisoning Decision Trees Stefano Calzavara Lorenzo Cazzaro Massimo Vettori AAML 30 0 0 01 Oct 2024
PACOL: Poisoning Attacks Against Continual Learners Huayu Li G. Ditzler AAML 25 2 0 18 Nov 2023
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations T. Krauß Alexandra Dmitrienko AAML 27 4 0 06 Jun 2023
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach M. Anisetti C. Ardagna Alessandro Balestrucci Nicola Bena Ernesto Damiani C. Yeun AAML OOD 32 10 0 28 Sep 2022
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers? Antonio Emanuele Cinà Sebastiano Vascon Ambra Demontis Battista Biggio Fabio Roli Marcello Pelillo AAML 32 9 0 23 Mar 2021
A Game-Theoretic Approach to Adversarial Linear Support Vector Classification Farhad Farokhi AAML 27 3 0 24 Jun 2019
Stronger Data Poisoning Attacks Break Data Sanitization Defenses Pang Wei Koh Jacob Steinhardt Percy Liang 6 240 0 02 Nov 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,113 0 04 Nov 2016