Provably Safeguarding a Classifier from OOD and Adversarial Samples

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SPIRE - Sciences Po Institutional REpository
Published
2025-04-24
Primary Topic
Adversarial Robustness in Machine Learning
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article
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article

Provably Safeguarding a Classifier from OOD and Adversarial Samples

Michèle Sébag, Johanne Cohen, Christophe Labreuche, Nicolas Atienza
SPIRE - Sciences Po Institutional REpository
Adversarial Robustness in Machine Learning
article

Provably Safeguarding a Classifier from OOD and Adversarial Samples

Michèle Sébag, Johanne Cohen, Christophe Labreuche, Nicolas Atienza
article en

Abstract

No abstract available for this paper.
SPIRE - Sciences Po Institutional REpository
Laboratoire de Recherche en Informatique (FR), Laboratoire Interdisciplinaire des Sciences du Numérique (FR)
Openalex Percentile: Top 81%
Adversarial Robustness in Machine Learning
Open Access PDFOpenAlex
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Provably Safeguarding a Classifier from OOD and Adversarial Samples — Michèle Sébag, Johanne Cohen, et al. · SPIRE - Sciences Po Institutional REpository (2025) | TGRS Research Map | TGRS