Improving Adversarial Robustness of Ensemble Classifiers by Diversified Feature Selection and Stochastic Aggregation

Learning-based classifiers are found to be vulnerable to attacks by adversarial samples. Some works suggested that ensemble classifiers tend to be more robust than single classifiers against evasion attacks. However, recent studies have shown that this is not necessarily the case under more realisti...

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Principais autores:	Fuyong Zhang, Kuan Li, Ziliang Ren
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2024-03-01
coleção:	Mathematics
Assuntos:	adversarial machine learning evasion attacks classifier robustness ensemble classifiers gradient correlation
Acesso em linha:	https://www.mdpi.com/2227-7390/12/6/834
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